Virtual Machine Placement Literature Review
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paper_title: Energy-Aware Resource Allocation Heuristics for Efficient Management of Data Centers for Cloud Computing

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Cloud computing offers utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of electrical energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only minimize operational costs but also reduce the environmental impact. In this paper, we define an architectural framework and principles for energy-efficient Cloud computing. Based on this architecture, we present our vision, open research challenges, and resource provisioning and allocation algorithms for energy-efficient management of Cloud computing environments. The proposed energy-aware allocation heuristics provision data center resources to client applications in a way that improves energy efficiency of the data center, while delivering the negotiated Quality of Service (QoS). In particular, in this paper we conduct a survey of research in energy-efficient computing and propose: (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering QoS expectations and power usage characteristics of the devices; and (c) a number of open research challenges, addressing which can bring substantial benefits to both resource providers and consumers. We have validated our approach by conducting a performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant cost savings and demonstrates high potential for the improvement of energy efficiency under dynamic workload scenarios.
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paper_title: Survey of Energy Efficient Data Centers in Cloud Computing

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The rapid growing demand for computational power by modern applications in Cloud Computing has led to the creation of large-scale data centers. Such data centers consume huge amount of electrical energy, emitting a great deal of CO2 in our environment. One effective way so as to reduce energy consumption is consolidating the vms using dynamic migration on minimum number of nodes and switching idle nodes to the sleep or hibernate modes which can cause better resource usage. But, due to the varying workloads in applications, consolidating the vms can cause SLA violation specially in cases that resource usage exceeds upper threshold defined for such nodes including consolidated vms. On the one hand, due to the necessity of providing the high QoS for customers which is negotiated in terms of SLA in cloud computing and on the other hand, importance of energy consumption as a critical problem, there is the necessity in addressing performance-energy trade off - the minimization of energy consumption, at the same time to take into account QoS. In this paper, related works regarding energy efficient datacentres have been conducted and in the end they have been compared with each other.
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paper_title: Survey of virtual machine placement in federated clouds

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Cloud computing provides facility to its customers to dynamically scale up the applications, platform and the hardware infrastructure. But the resources provided from one cloud provider are finite and at some point of time can violate the SLA (service level agreements). One approach can be used to better facilitate the customers is to scale the applications, software platforms and the infrastructure to multiple independent clouds i.e. federated clouds. The federated clouds can share the resources with other cloud providers as the scale and load increases and can pay for the service on usage based. Virtual machine allocation is also an important parameter of federated clouds, because multiple clouds are exchanging the VM (Virtual Machine) with one another and the trading policies of all the clouds is not same. VM Allocation can be optimized for cost-effective Virtual machine allocation. This paper is a survey of all VM allocation policies available in federated clouds.
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paper_title: Comparing VM-Placement Algorithms for On-Demand Clouds

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Much recent research has been devoted to investigating algorithms for allocating virtual machines (VMs) to physical machines (PMs) in infrastructure clouds. Many such algorithms address distinct problems, such as initial placement, consolidation, or tradeoffs between honoring service-level agreements and constraining provider operating costs. Even where similar problems are addressed, each individual research team evaluates proposed algorithms under distinct conditions, using various techniques, often targeted to a small collection of VMs and PMs. In this paper, we describe an objective method that can be used to compare VM-placement algorithms in large clouds, covering tens of thousands of PMs and hundreds of thousands of VMs. We demonstrate our method by comparing 18 algorithms for initial VM placement in on-demand infrastructure clouds. We compare algorithms inspired by open-source code for infrastructure clouds, and by the online bin-packing literature.
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paper_title: Virtual Machine Placement Literature Review

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Cloud Computing Datacenters host millions of virtual machines (VMs) on real world scenarios. In this context, Virtual Machine Placement (VMP) is one of the most challenging problems in cloud infrastructure management, considering also the large number of possible optimization criteria and different formulations that could be studied. VMP literature include relevant topics such as energy-efficiency, Service Level Agreements (SLA), cloud service markets, Quality of Service (QoS) and carbon dioxide emissions, all of them with high economical and ecological impact. This work presents an extensive up-to-date review of the most relevant VMP literature in order to identify research opportunities.
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paper_title: Virtual Machine Placement Literature Review

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Cloud Computing Datacenters host millions of virtual machines (VMs) on real world scenarios. In this context, Virtual Machine Placement (VMP) is one of the most challenging problems in cloud infrastructure management, considering also the large number of possible optimization criteria and different formulations that could be studied. VMP literature include relevant topics such as energy-efficiency, Service Level Agreements (SLA), cloud service markets, Quality of Service (QoS) and carbon dioxide emissions, all of them with high economical and ecological impact. This work presents an extensive up-to-date review of the most relevant VMP literature in order to identify research opportunities.
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paper_title: Virtual Machine Placement Literature Review

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Cloud Computing Datacenters host millions of virtual machines (VMs) on real world scenarios. In this context, Virtual Machine Placement (VMP) is one of the most challenging problems in cloud infrastructure management, considering also the large number of possible optimization criteria and different formulations that could be studied. VMP literature include relevant topics such as energy-efficiency, Service Level Agreements (SLA), cloud service markets, Quality of Service (QoS) and carbon dioxide emissions, all of them with high economical and ecological impact. This work presents an extensive up-to-date review of the most relevant VMP literature in order to identify research opportunities.
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paper_title: Guaranteeing High Availability Goals for Virtual Machine Placement

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The placement of virtual machines (VMs) on a cluster of hosts under multiple constraints, including administrative (security, regulations) resource-oriented (capacity, energy), and QoS-oriented (performance) is a highly complex task. We define a new high-availability property for a VM, when a VM is marked as k-resilient, as long as there are up to k host failures, it should be guaranteed that it can be relocated to a non-failed host without relocating other VMs. Together with Hardware Predictive Failure Analysis and live migration, which enable VMs to be evacuated from a host before it fails, this property allows the continuous running of VMs on the cluster despite host failures. The complexity of the constraints associated with k-resiliency, which are naturally expressed by Second Order logic statements, prevented their integration into the placement computation until now. We present a novel algorithm which enables this integration by transforming the k-resiliency constraints to rules consumable by a generic Constraint Programming engine, prove that it guarantees the required resiliency and describe the implementation. We provide some preliminary results and compare our high availability support with naive solutions.
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paper_title: Reducing electricity cost through virtual machine placement in high performance computing clouds

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In this paper, we first study the impact of load placement policies on cooling and maximum data center temperatures in cloud service providers that operate multiple geographically distributed data centers. Based on this study, we then propose dynamic load distribution policies that consider all electricity-related costs as well as transient cooling effects. Our evaluation studies the ability of different cooling strategies to handle load spikes, compares the behaviors of our dynamic cost-aware policies to cost-unaware and static policies, and explores the effects of many parameter settings. Among other interesting results, we demonstrate that (1) our policies can provide large cost savings, (2) load migration enables savings in many scenarios, and (3) all electricity-related costs must be considered at the same time for higher and consistent cost savings.
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paper_title: On Theory of VM Placement: Anomalies in Existing Methodologies and Their Mitigation Using a Novel Vector Based Approach

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In this paper, we present the methodologies used in existing literature for Virtual Machine (VM) placement, load balancing and server consolidation in a data center environment. While the methodologies may seem fine on the surface, certain drawbacks and anomalies can be uncovered when they are analyzed deeper. We point out those anomalies and draw backs in the existing literature and explain what are the root causes of such anomalies. Then we propose a novel methodology based on vector arithmetic which not only addresses those anomalies but also leads to some interesting theories and algorithms to tackle the above mentioned three functionalities required in managing resources of data centers. We believe that with a strong mathematical base, our methodology has the potential to become the foundation of future models and algorithms in this research area.
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paper_title: A Two Stage Heuristic Algorithm for Solving the Server Consolidation Problem with Item-Item and Bin-Item Incompatibility Constraints

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The problem of server sprawl is common in data centers of most business organizations. It is most often the case that an application is run on dedicated servers. This leads to situations where organizations end up having numerous servers that remain under-utilized most of the times. The servers, in such scenarios, are allocated more resources (disk, cpu and memory) than are justified by their present workloads. Consolidating multiple underutilized servers into a fewer number of non-dedicated servers that can host multiple applications is an effective tool for businesses to enhance their returns on investment. The problem can be modeled as a variant of the bin packing problem where items to be packed are the servers being consolidated and bins are the target servers. The sizes of the servers/items being packed are resource utilizations which are obtained from the performance trace data. Here we describe a novel two stage heuristic algorithm for taking care of the "bin-item" incompatibility constraints that are inherent in any server consolidation problem. The model is able to solve extremely large instances of problem in a reasonable amount of time.
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paper_title: Improving Performance and Availability of Services Hosted on IaaS Clouds with Structural Constraint-Aware Virtual Machine Placement

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The increasing popularity of modern virtualization-based datacenters continues to motivate both industry and academia to provide answers to a large variety of new and challenging questions. In this paper we aim to answer focusing on one such question: how to improve performance and availability of services hosted on IaaS clouds. Our system, structural constraint-aware virtual machine placement (SCAVP), supports three types of constraints: demand, communication and availability. We formulate SCAVP as an optimization problem and show its hardness. We design a hierarchical placement approach with four approximation algorithms that efficiently solves the SCAVP problem for large problem sizes. We provide a formal model for the application (to better understand structural constraints) and the datacenter (to effectively capture capabilities), and use the two models as inputs to the placement problem. We evaluate SCAVP in a simulated environment to illustrate the efficiency and importance of the proposed approach.
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paper_title: Optimal virtual machine placement across multiple cloud providers

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Cloud computing provides users an efficient way to dynamically allocate computing resources to meet demands. Cloud providers can offer users two payment plans, i.e., reservation and on-demand plans for resource provisioning. Price of resources in reservation plan is generally cheaper than that in on-demand plan. However, since the reservation plan has to be acquired in advance, it may not fully meet future demands in which the on-demand plan can be used to guarantee the availability to the user. In this paper, we propose an optimal virtual machine placement (OVMP) algorithm. This algorithm can minimize the cost spending in each plan for hosting virtual machines in a multiple cloud provider environment under future demand and price uncertainty. OVMP algorithm makes a decision based on the optimal solution of stochastic integer programming (SIP) to rent resources from cloud providers. The performance of OVMP algorithm is evaluated by numerical studies and simulation. The results clearly show that the proposed OVMP algorithm can minimize users' budgets. This algorithm can be applied to provision resources in emerging cloud computing environments.
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paper_title: An energy aware framework for virtual machine placement in cloud federated data centres

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Data centres are powerful ICT facilities which constantly evolve in size, complexity, and power consumption. At the same time users' and operators' requirements become more and more complex. However, existing data centre frameworks do not typically take energy consumption into account as a key parameter of the data centre's configuration. To lower the power consumption while fulfilling performance requirements we propose a flexible and energy-aware framework for the (re)allocation of virtual machines in a data centre. The framework, being independent from the data centre management system, computes and enacts the best possible placement of virtual machines based on constraints expressed through service level agreements. The framework's flexibility is achieved by decoupling the expressed constraints from the algorithms using the Constraint Programming (CP) paradigm and programming language, basing ourselves on a cluster management library called Entropy. Finally, the experimental and simulation results demonstrate the effectiveness of this approach in achieving the pursued energy optimization goals.
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paper_title: Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments

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Server consolidation using virtualization technology has become increasingly important for improving data center efficiency. It enables one physical server to host multiple independent virtual machines (VMs), and the transparent movement of workloads from one server to another. Fine-grained virtual machine resource allocation and reallocation are possible in order to meet the performance targets of applications running on virtual machines. On the other hand, these capabilities create demands on system management, especially for large-scale data centers. In this paper, a two-level control system is proposed to manage the mappings of workloads to VMs and VMs to physical resources. The focus is on the VM placement problem which is posed as a multi-objective optimization problem of simultaneously minimizing total resource wastage, power consumption and thermal dissipation costs. An improved genetic algorithm with fuzzy multi-objective evaluation is proposed for efficiently searching the large solution space and conveniently combining possibly conflicting objectives. The simulation-based evaluation using power-consumption and thermal-dissipation models based on profiling of a Blade Center, demonstrates the good performance, scalability and robustness of our proposed approach. Compared with four well-known bin-packing algorithms and two single-objective approaches, the solutions obtained from our approach seek good balance among the conflicting objectives while others cannot.
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paper_title: Profiling Applications for Virtual Machine Placement in Clouds

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Application profiling is an important technique for efficient resource management. The decision making of scheduling and resource allocation typically takes great advantage of such a technique primarily for improving resource utilization. With the advent of cloud computing as a multitenant virtualized platform, diverse applications are increasingly deployed onto the cloud and they more than often share physical resources. The background load (other applications running on the same physical machine) is therefore an important factor for profiling an application in this cloud computing scenario. In this paper, we present a novel application profiling technique using the canonical correlation analysis (CCA) method, which identifies the relationship between application performance and resource usage. We further devise a performance prediction model based on application profiles generated using CCA. Clearly, our profiling technique with this prediction model has a lot of potentials particularly in virtual machine (VM) placement with performance awareness. Our experimental results demonstrate the capability of our profiling technique and the accuracy of our prediction model.
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paper_title: Towards Profitable Virtual Machine Placement in the Data Center

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Motivated by the limit on the power usage effectiveness (PUE) of the data centers, the potential benefit of the consolidation, and the impetus of achieving maximum return on investment (ROI) on the cloud computing market, we investigate VM placement in the data center, formulate a multi-level generalized assignment problem (MGAP) for maximizing the profit under the service level agreement and the power budget constraint based on the model of a virtualized data center, and solve it with a first-fit heuristic. Numerical simulations show that the first-fit heuristic is effective in solving the large-scale instances of the MGAP with the sampled simulation setups.
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paper_title: SLA guaranteed virtual machine consolidation for computing clouds

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One of the most attractive features that computing clouds can offer is that the data center maintenance cost can be reduced by consolidating the workload generated by the virtual machines (VMs) into a few powerful physical servers. In this paper, the problem of maximally consolidating heterogeneous virtual machines into servers while protecting the service level agreement of each virtual machine is investigated. A generic and robust VM workload consolidation framework is proposed. The proposed framework achieves several attractive properties like low maintenance cost, low algorithm complexity, adjustable resource provisioning aggressiveness, and good scalability.
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paper_title: Evolutionary Algorithms for Solving Multi-Objective Problems

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List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.
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paper_title: Higher SLA satisfaction in datacenters with continuous VM placement constraints

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In a virtualized datacenter, the Service Level Agreement for an application restricts the Virtual Machines (VMs) placement. An algorithm is in charge of maintaining a placement compatible with the stated constraints. Conventionally, when a placement algorithm computes a schedule of actions to re-arrange the VMs, the constraints ignore the intermediate states of the datacenter to only restrict the resulting placement. This situation may lead to temporary violations of the constraints. In this paper, we discuss the causes of these violations. We then advocate for continuous placement constraints to restrict also the actions schedule. We discuss why their development requires more attention and how the extensible placement algorithm BtrPlace can address this issue.
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paper_title: A virtual machine consolidation framework for MapReduce enabled computing clouds

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In nowadays computing clouds, it is of the cloud providers' economic interests to correctly consolidate the workload of the virtual machines (VMs) into the suitable physical servers in the cloud data center in order to minimize the total maintenance cost. However, during the consolidation process, sufficient protection should be provided to the service level agreement (SLA) of the VMs. In this paper, the VM consolidation problem for MapReduce enabled computing clouds has been investigated. In the MapReduce enabled computing clouds, MapReduce jobs are carried out by homogeneous MapReduce VM instances that have identical hardware resource. Two resource allocation schemes with corresponding SLA constraints for the MapReduce VMs and the non-MapReduce VMs are proposed. Based on these schemes, the VM consolidation problem is modeled as an integer nonlinear optimization problem and an efficient algorithm has been proposed to locate its solutions. The results show that better VM consolidation performance can be achieved by colocating MapReduce instances together with non-MapReduce instances in the same set of physical servers.
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paper_title: Intelligent virtual machine placement for cost efficiency in geo-distributed cloud systems

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An important challenge of running large-scale cloud services in a geo-distributed cloud system is to minimize the overall operating cost. The operating cost of such a system includes two major components: electricity cost and wide-area-network (WAN) communication cost. While the WAN communication cost is minimized when all virtual machines (VMs) are placed in one datacenter, the high workload at one location requires extra power for cooling facility and results in worse power usage effectiveness (PUE). In this paper, we develop a model to capture the intrinsic trade-off between electricity and WAN communication costs, and formulate the optimal VM placement problem, which is NP-hard due to its binary and quadratic nature. While exhaustive search is not feasible for large-scale scenarios, heuristics which only minimize one of the two cost terms yield less optimized results. We propose a cost-aware two-phase metaheuristic algorithm, Cut-and-Search, that approximates the best trade-off point between the two cost terms. We evaluate Cut-and-Search by simulating it over multiple cloud service patterns. The results show that the operating cost has great potential of improvement via optimal VM placement. Cut-and-Search achieves a highly optimized trade-off point within reasonable computation time, and outperforms random placement by 50%, and the partial-optimizing heuristics by 10-20%.
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paper_title: Energy-aware virtual machine placement in data centers

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The energy efficiency of modern data centers has become a practical concern and has attracted significant attention in recent years. In contract to existing solutions that primarily focuses on only one specific aspect of management to reduce energy consumption, this paper explores the balance between server energy consumption and network energy consumption to present an energy-aware joint virtual machine (VM) placement. Given the definition of VM placement fairness, the basic algorithm of VM placement which fulfills server energy consumption constraints is conducted. Then, we further formulate the VM placement as an optimization problem which considers application dependencies to reduce network energy consumption. We design a joint algorithm that efficiently solves the VM placement problem for very large problem sizes. Using simulations, we conduct a comparative analysis on the impact of the data center architectures, server constraints and application dependencies on the potential performance gain of energy-aware VM placement. Compared to existing generic methods, we show a significant performance improvement such as efficiently reducing the number of physical machines used to save server energy consumption, decreasing the communication distance between VMs to obtain data center network energy consumption efficiency, improving scalability of data centers.
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paper_title: Multi-objective pump scheduling optimisation using evolutionary strategies

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Multi-objective Evolutionary Algorithms (MOEAs) are used to solve an optimal pump-scheduling problem with four objectives to be minimized: electric energy cost, maintenance cost, maximum power peak, and level variation in a reservoir. Six different MOEAs were implemented and compared. In order to consider hydraulic and technical constraints, a heuristic algorithm was developed and combined with each implemented MOEA. Evaluation of experimental results of a set of metrics shows that the Strength Pareto Evolutionary Algorithm achieves better overall performance than other MOEAs for the parameters considered in the test problem, providing a wide range of optimal pump schedules to chose from.
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paper_title: An energy-efficient approach for virtual machine placement in cloud based data centers

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Cloud computing is a new technology which is proffering IT services based on pay-as-you-go model to consumers from everywhere in the world. The growing demand of Cloud infrastructure and modern computational requests like business, scientific and web applications result in large-scale data centers and lead to extra electrical energy consumption. High energy consumption causes high operational cost and also led to high carbon emission which is harmful for atmosphere. Hence, energy-efficient techniques are required to minimize the negative effects of Cloud computing on the environment. Virtual machines (VMs) migration, dynamic consolidation in the virtualized data centers in cloud environments and switching idle physical machines off could yield reduce energy consumption; hence, one of the important issues for energy efficiency in virtualized cloud environments is how to place new VMs or selected VMs for migration across the hosts. In this paper we propose an energy-efficient approach based on Minimum Correlation Coefficient (MCC) method for virtual machine placement in cloud based, virtualized data centers. The proposed approach regards both Service Level Agreement (SLA) and low energy consumption and tries to make a trade-off between these two concerns using fuzzy Analytic Hierarchy Process (AHP). We evaluate our approach using Cloudsim toolkit as a modern cloud computing environment simulator. The evaluation shows that our proposed method offers a suitable trade-off between power efficiency and SLA violation reduction in cloud data centers.
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paper_title: Joint VM placement and routing for data center traffic engineering

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Today's data centers need efficient traffic management to improve resource utilization in their networks. In this work, we study a joint tenant (e.g., server or virtual machine) placement and routing problem to minimize traffic costs. These two complementary degrees of freedom—placement and routing—are mutually-dependent, however, are often optimized separately in today's data centers. Leveraging and expanding the technique of Markov approximation, we propose an efficient online algorithm in a dynamic environment under changing traffic loads. The algorithm requires a very small number of virtual machine migrations and is easy to implement in practice. Performance evaluation that employs the real data center traffic traces under a spectrum of elephant and mice flows, demonstrates a consistent and significant improvement over the benchmark achieved by common heuristics.
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paper_title: VMPlanner : Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers

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In recent years, the power costs of cloud data centers have become a practical concern and have attracted significant attention from both industry and academia. Most of the early works on data center energy efficiency have focused on the biggest power consumers (i.e., computer servers and cooling systems), yet without taking the networking part into consideration. However, recent studies have revealed that the network elements consume 10-20% of the total power in the data center, which poses a great challenge to effectively reducing network power cost without adversely affecting overall network performance. Based on the analysis on topology characteristics and traffic patterns of data centers, this paper presents a novel approach, called VMPlanner, for network power reduction in the virtualization-based data centers. The basic idea of VMPlanner is to optimize both virtual machine placement and traffic flow routing so as to turn off as many unneeded network elements as possible for power saving. We formulate the optimization problem, analyze its hardness, and solve it by designing VMPlanner as a stepwise optimization approach with three approximation algorithms. VMPlanner is implemented and evaluated in a simulated environment with traffic traces collected from a data center test-bed, and the experiment results illustrate the efficacy and efficiency of this approach.
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paper_title: Multiobjective Virtual Machine Placement in Cloud Environment

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In this paper, the virtual machine placement problem is formulated as a multi-objective optimization problem. The objectives are maximizing profit, maximizing load balancing and minimizing recourse wastage. Results of Genetic algorithms, Non-dominated Sorting Genetic Algorithm and Non-dominated Sorting Genetic Algorithm-II are compared with common solution representation, penalty and benefit values. All the three algorithms reported good solutions whereas GA and NSGA are subjected to premature convergence and duplicate solutions. NSGA-II gives good and diversified range of solutions.
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paper_title: Time-Aware VM-Placement and Routing with Bandwidth Guarantees in Green Cloud Data Centers

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Variation in network performance due to the shared resources is a key obstacle for cloud adoption. Thus, the success of cloud providers to attract more tenants depends on their ability to provide bandwidth guarantees. Power efficiency in data centers has become critically important for supporting larger number of tenants. In this paper, we address the problem of time-aware VM-placement and routing (TVPR), where each tenant requests for a specified amount of server resources (VMs) and network resource (bandwidth) for a given duration. The TVPR problem allocates the required resources for as many tenants as possible by finding the right set of servers to map their VMs and routing their traffic so as to minimize the total power consumption. We propose a multi-component utilization-based power model to determine the total power consumption of a data center according to the resource utilization of the components (servers and switches). We then develop a mixed integer linear programming (MILP) optimization problem formulation based on the proposed power model and prove it to be N P-complete. Since the TVPR problem is computationally prohibitive, we develop a fast and scalable heuristic algorithm. To demonstrate the efficiency of our proposed algorithm, we compare its performance with the numerical results obtained by solving the MILP problem using CPLEX, for a small data center. We then demonstrate the effectiveness of the proposed algorithm in terms of power consumption and acceptance ratio for large data centers through simulation results.
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paper_title: Virtual Machine Placement Optimization Supporting Performance SLAs

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Cloud computing model separates usage from ownership in terms of control on resource provisioning. Resources in the cloud are projected as a service and are realized using various service models like IaaS, PaaS and SaaS. In IaaS model, end users get to use a VM whose capacity they can specify but not the placement on a specific host or with which other VMs it can be co-hosted. Typically, the placement decisions happen based on the goals like minimizing the number of physical hosts to support a given set of VMs by satisfying each VMs capacity requirement. However, the role of the VMM usage to support I/O specific workloads inside a VM can make this capacity requirement incomplete. I/O workloads inside VMs require substantial VMM CPU cycles to support their performance. As a result, placement algorithms need to include the VMM's usage on a per VM basis. Secondly, cloud centers encounter situations wherein change in existing VM's capacity or launching of new VMs need to be considered during different placement intervals. Usually, this change is handled by migrating existing VMs to meet the goal of optimal placement. We argue that VM migration is not a trivial task and does include loss of performance during migration. We quantify this migration overhead based on the VM's workload type and include the same in placement problem. One of the goals of the placement algorithm is to reduce the VM's migration prospects, thereby reducing chances of performance loss during migration. This paper evaluates the existing ILP and First Fit Decreasing (FFD) algorithms to consider these constraints to arrive at placement decisions. We observe that ILP algorithm yields optimal results but needs long computing time even with parallel version. However, FFD heuristics are much faster and scalable algorithms that generate a sub-optimal solution, as compared to ILP, but in time-scales that are useful in real-time decision making. We also observe that including VM migration overheads in the placement algorithm results in a marginal increase in the number of physical hosts but a significant, of about 84 percent reduction in VM migration.
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paper_title: Design and Implementation of a Multi-objective Optimization Mechanism for Virtual Machine Placement in Cloud Computing Data Center

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Cloud computing is becoming a popular way of supplying and using computing resources. A cloud-computing data center is equipped with a large number of physical resources and must manage an even larger number of virtual machines (VMs). The center’s VM placement strategy affects the utilization of physical resources, and consequently, it influences operational costs. Our goal is to develop a multi-objective optimization mechanism for VM placement that satisfies various constraints and results in the lowest operational cost. The number of possible combinations of VMs and hosts can be extremely large. For the mechanism to be practical, the number of possible combinations must be reduced. We reduced computational overheads by classifying VM hosts into a relatively small number of equivalent sets. Simulation results show that expected operational costs can be significantly reduced by applying the proposed mechanism.
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paper_title: An Availability-Aware Virtual Machine Placement Approach for Dynamic Scaling of Cloud Applications

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Cloud computing promises customers the on-demand ability to dynamically provision virtualization resources in face of workload variations. Most existing scaling approaches addressed this problem by allocating application to a certain amount of cloud resources. However, the problem of the availability of application influenced by VM-based physical locations during resource scaling process is a serious challenge due to dynamic complex workload and has not been widely discussed yet. In this paper, we present a novel availability-based computing model to describe availability attribute of one application in the hierarchical structured cloud. Moreover, we propose an availability-aware policy by performing both vertical and horizontal scaling to explore how and where to allocate computing resource. Simulation results indicate that our model captured the availability of cloud applications properly and proposed scaling approach achieves the objectives of meeting availability demands and minimizing the total communication cost.
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paper_title: VM Placement Strategies for Cloud Scenarios

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The problem of Virtual Machine (VM) placement in a compute cloud infrastructure is well-studied in the literature. However, the majority of the existing works ignore the dynamic nature of the incoming stream of VM deployment requests that continuously arrive to the cloud provider infrastructure. In this paper we provide a practical model of cloud placement management under a stream of requests and present a novel technique called Backward Speculative Placement (BSP)that projects the past demand behavior of a VM to a candidate target host. We exploit the BSP technique in two algorithms, first for handling the stream of deployment requests, second in a periodic optimization, to handle the dynamic aspects of the demands. We show the benefits of our BSPtechnique by comparing the results on a simulation period with a strategy of choosing an optimal placement at each time instant, produced by a generic MIP solver.
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paper_title: Virtual Machine Placement for Improving Energy Efficiency and Network Performance in IaaS Cloud

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Under the premise of ensuring application performance, how to place virtual machines (VMs) on physical machines (PMs) to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers in IaaS cloud. The existing VM placement schemes are mostly to reduce energy consumption by optimizing utilization of physical server or network elements, but the issue of aggressive consolidation of VM is ignored, which may lead to network performance degradation. To address the issue, this paper proposes a VM placement scheme based on a new two-stage heuristic algorithm to optimize network performance and to reduce energy consumption of physical servers and network elements, so as to finally achieve the tradeoff between energy efficiency and network performance. The simulations show that our solution achieves good results.
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paper_title: Energy-Saving Virtual Machine Placement in Cloud Data Centers

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In cloud data centers, different mapping relationships between virtual machines (VMs) and physical machines (PMs) cause different resource utilization, therefore, how to place VMs on PMs to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers. The existing VM placement schemes are to optimize physical server resources utilization or network resources utilization, but few of them focuses on optimizing multiple resources utilization simultaneously. To address the issue, this paper proposes a VM placement scheme meeting multiple resource constraints, such as the physical server size (CPU, memory, storage, bandwidth, etc.) and network link capacity to improve resource utilization and reduce both the number of active physical servers and network elements so as to finally reduce energy consumption. Since VM placement problem is abstracted as a combination of bin packing problem and quadratic assignment problem, which is also known as a classic combinatorial optimization and NP-hard problem, we design a novel greedy algorithm by combining minimum cut with the best-fit, and the simulations show that our solution achieves better results.
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paper_title: Energy-Efficient Virtual Machine Placement in Data Centers by Genetic Algorithm

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Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches to the virtual machine placement problem consider the energy consumption by physical machines in a data center only, but do not consider the energy consumption in communication network in the data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement in order to make the data center more energy-efficient. In this paper, we propose a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both the servers and the communication network in the data center. Experimental results show that the genetic algorithm performs well when tackling test problems of different kinds, and scales up well when the problem size increases.
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paper_title: EQVMP: Energy-efficient and QoS-aware virtual machine placement for software defined datacenter networks

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To provide effective and reliable services, cloud datacenters need parallel computing and virtualization techniques. This work presents an improved virtual machine (VM) placement mechanism, called Energy efficiency and Quality of Service (QoS) aware VM Placement (EQVMP) to overcome the problem of unbalanced traffic load in switching on and off VMs for the purpose of energy saving. EQVMP combines three key techniques: (1) hop reduction, (2) energy saving and (3) load balancing. Hop reduction can regroup VMs to lower the traffic load among them. Energy saving techniques aim at choosing the appropriate servers. The proposed load balancing updates VM placement periodically. Our experimental results show that the proposed scheme can lower energy consumption and maintain QoS. We propose an evaluation score [1] to assess VM placement in terms of energy, delay and throughput. Comparing to other existing placement policies, our proposed mechanism can enhance system throughput by 25% and can have better evaluation score.
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paper_title: Optimal update frequency model for physical machine state change and virtual machine placement in the cloud

paper_content:

Cloud computing is evolving into the default operational framework running modern data centers. Efficient data center operation is concerned with the total amount of energy consumed as well as assuring adequate resources are available to process all of the incoming work requests. Existing research has demonstrated several algorithms that can be used to determine the optimal number of resources required to service these requests. However, a key issue not addressed in these algorithms is determining the frequency of recalculating the number of required resources. Changing the required resources at a rate slower than the optimal update frequency results in lower energy efficiency due to the over allocation of resources. Changing the resources at a rate higher than the optimal frequency results in insufficient time for systems to change state, which results in SLA violations. In this paper, a stochastic optimization model is presented that determines the optimal update frequency for changing the states of the nodes of the cloud as well as determining the proper frequency for recalculating the maximum expected load, which improves the determination of the optimum number of resources required, therefore maximizes energy efficiency and minimizes SLA violations.
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paper_title: Energy Efficient VM Placement Supported by Data Analytic Service

paper_content:

The popularity and commercial use of cloud computing has prompted an increased concern among cloud service providers for energy efficiency while still maintaining quality of service. One of the key techniques used for the efficient use of cloud server resources is virtual machine placement. This work introduces a precise VM placement algorithm that ensures energy efficiency and also prevents Service Level Agreement (SLA) violation. The mathematical model of the algorithm is supported by a sophisticated data analytic system implemented as a service. The precision of the algorithm is achieved by allowing each individual VM to build its own data model on demand over an appropriate time horizon. Thus the data model can reflect accurately the characteristics of resource usage of the VM. The algorithm can communicate synchronously or asynchronously with the data analytic service which is deployed as a cloud-based solution. In the experiments, several advanced data modelling and use forecasting techniques were evaluated. Results from simulation-based experiments show that the VM placement algorithm (supported by the data analytic service) can effectively reduce power consumption, the number of VM migrations, and prevent SLA violation, it also compares very favourably with other placement algorithms.
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paper_title: Power-Efficient Virtual Machine Placement and Migration in Data Centers

paper_content:

In this paper, we propose a power-efficient solution for virtual machine placement and migration in a fat tree data center network. This solution reduces power consumption as well as job delay by aggregating virtual machines to a few hyper visors and migrating communicating parties to close locations. In this work, we consider OpenFlow as the implementation protocol. In an OpenFlow environment, a centralized controller oversees job loads, virtual machine requirements and hardware availability. Given observation of such global knowledge, the OpenFlow controller can schedule jobs and distribute virtual machines accordingly. As jobs change and flows shift, the OpenFlow controller dynamically adjusts virtual machine assignments by aggregating virtual machines to close locations in order to save energy. With this placement and migration proposal, more jobs can operate concurrently with close sources and destinations of flows, thus both job and flow delay can be reduced.
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paper_title: Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments

paper_content:

Server consolidation using virtualization technology has become increasingly important for improving data center efficiency. It enables one physical server to host multiple independent virtual machines (VMs), and the transparent movement of workloads from one server to another. Fine-grained virtual machine resource allocation and reallocation are possible in order to meet the performance targets of applications running on virtual machines. On the other hand, these capabilities create demands on system management, especially for large-scale data centers. In this paper, a two-level control system is proposed to manage the mappings of workloads to VMs and VMs to physical resources. The focus is on the VM placement problem which is posed as a multi-objective optimization problem of simultaneously minimizing total resource wastage, power consumption and thermal dissipation costs. An improved genetic algorithm with fuzzy multi-objective evaluation is proposed for efficiently searching the large solution space and conveniently combining possibly conflicting objectives. The simulation-based evaluation using power-consumption and thermal-dissipation models based on profiling of a Blade Center, demonstrates the good performance, scalability and robustness of our proposed approach. Compared with four well-known bin-packing algorithms and two single-objective approaches, the solutions obtained from our approach seek good balance among the conflicting objectives while others cannot.
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paper_title: Server consolidation with migration control for virtualized data centers

paper_content:

Virtualization has become a key technology for simplifying service management and reducing energy costs in data centers. One of the challenges faced by data centers is to decide when, how, and which virtual machines (VMs) have to be consolidated into a single physical server. Server consolidation involves VM migration, which has a direct impact on service response time. Most of the existing solutions for server consolidation rely on eager migrations, which try to minimize the number of physical servers running VMs. These solutions generate unnecessary migrations due to unpredictable workloads that require VM resizing. This paper proposes an LP formulation and heuristics to control VM migration, which prioritize virtual machines with steady capacity. We performed experiments using TU-Berlin and Google data center workloads to compare our migration control strategy against existing eager-migration-based solutions. We observed that avoiding migration of VMs with steady capacity reduces the number of migrations with minimal penalty in the number of physical servers.
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paper_title: Inter-and-intra data center VM-placement for energy-efficient large-Scale cloud systems

paper_content:

Data centers play the crucial role in the delivery of Cloud services by enabling on-demand access to the shared resources such as software, platform, and infrastructure. Virtual Machine (VM) allocation is one of the challenging tasks in the data center management since user requirements, typically expressed as SLAs, have to be met with the minimum operational expenditure. Despite their huge processing and storage facilities, data centers are among the major contributors of the GreenHouse Gas (GHG) emissions of the IT services. In this paper, we propose a holistic approach for a large-scale Cloud system where the Cloud services are provisioned by several data centers interconnected over the backbone network. We propose a Mixed Integer Linear Programming (MILP) formulation that aims at virtualizing the backbone topology and placing the VMs in data centers with the objective of minimum power consumption. We compare our proposed solution with a benchmark MILP model which selects the closest data centers that can accommodate the user requests taking into account the CPU, memory, and bandwidth capacities of the residing physical hosts. Collected experimental results show the benefit of the proposed management scheme in terms of power consumption, resource utilization, and fairness for medium size data centers.
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paper_title: Efficient VM placement with multiple deterministic and stochastic resources in data centers

paper_content:

Virtual machines (VMs) may significantly improve the efficiency of data center infrastructure by sharing resources of physical servers. This benefit relies on an efficient VM placement scheme to minimize the number of required servers. Existing VM placement algorithms usually assume that VMs' demands for resources are deterministic and stable. However, for certain resources, such as network bandwidth, VMs' demands are bursty and time varying, and demonstrate stochastic nature. In this paper, we study efficient VM placement in data centers with multiple deterministic and stochastic resources. First, we formulate the Multidimensional Stochastic VM Placement (MSVP) problem, with the objective to minimize the number of required servers and at the same time satisfy a predefined resource availability guarantee. Then, we show that the problem is NP-hard, and propose a polynomial time algorithm called Max-Min Multidimensional Stochastic Bin Packing (M3SBP). The basic idea is to maximize the minimum utilization ratio of all the resources of a server, while satisfying the demands of VMs for both deterministic and stochastic resources. Next, we conduct simulations to evaluate the performance of M3SBP. The results demonstrate that M3SBP guarantees the availability requirement for stochastic resources, and M3SBP needs the smallest number of servers to provide the guarantee among the benchmark algorithms.
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paper_title: Dynamic optimization of virtual machine placement by resource usage prediction

paper_content:

In order to save energy with preventing the lack of the resources on servers in data centers, we address the dynamic optimization of virtual machine placement. A decision problem of the virtual machine placement has been formulated as Bin Packing problem. Conventional methods repeat to solve the problem at regular intervals. Live migration is used for changing the virtual machine placement, but some of live migrations are unnecessary; the virtual machine is migrated repeatedly between the physical servers. For the purpose of reducing the unnecessary live migrations, we propose a dynamic optimization of virtual machine placement by resource usage prediction. The proposed method predicts the future resource usage by Auto Regressive Model. The proposed method decides the virtual machine placement by solving the Bin Packing problem with the predicted resource usage.
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paper_title: An Optimization-Based Scheme for Efficient Virtual Machine Placement

paper_content:

According to the important methodology of convex optimization theory, the energy-efficient and scalability problems of modern data centers are studied. Then a novel virtual machine (VM) placement scheme is proposed for solving these problems in large scale. Firstly, by referring the definition of VM placement fairness and utility function, the basic algorithm of VM placement which fulfills server constraints of physical machines is discussed. Then, we abstract the VM placement as an optimization problem which considers the inherent dependencies and traffic between VMs. By given the structural differences of recently proposed data center architectures, we further investigate a comparative analysis on the impact of the network architectures, server constraints and application dependencies on the potential performance gain of optimization-based VM placement. Comparing with the existing schemes, the performance improvements are illustrated from multiple perspectives, such as reducing the number of physical machines deployment, decreasing communication cost between VMs, improving energy-efficient and scalability of data centers.
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paper_title: Multi-objective Virtual Machine Placement with Service Level Agreement: A Memetic Algorithm Approach

paper_content:

The process of selecting which virtual machines should be located (i.e. executed) at each physical machine of a Datacenter is known as Virtual Machine Placement VMP. This work proposes for the first time a multi-objective formulation of the VMP considering Service Level Agreement. A novel multiobjective memetic algorithm is also proposed to solve the formulated multi-objective problem. This proposal is validated comparing experimental results of the proposed algorithm with a brute force exhaustive search algorithm. Simulations prove the correctness of the proposed memetic algorithm and its scalability considering different experimental scenarios.
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paper_title: Improved energy-efficiency in cloud datacenters with interference-aware virtual machine placement

paper_content:

Virtualization is one of the main technologies used for improving resource efficiency in datacenters; it allows the deployment of co-existing computing environments over the same hardware infrastructure. However, the co-existing of environments — along with management inefficiencies — often creates scenarios of high-competition for resources between running workloads, leading to performance degradation. This phenomenon is known as Performance Interference, and introduces a non-negligible overhead that affects both a datacenter's Quality of Service and its energy-efficiency. This paper introduces a novel approach to workload allocation that improves energy-efficiency in Cloud datacenters by taking into account their workload heterogeneity. We analyze the impact of performance interference on energy-efficiency using workload characteristics identified from a real Cloud environment, and develop a model that implements various decision-making techniques intelligently to select the best workload host according to its internal interference level. Our experimental results show reductions in interference by 27.5% and increased energy-efficiency up to 15% in contrast to current mechanisms for workload allocation.
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paper_title: Guaranteeing High Availability Goals for Virtual Machine Placement

paper_content:

The placement of virtual machines (VMs) on a cluster of hosts under multiple constraints, including administrative (security, regulations) resource-oriented (capacity, energy), and QoS-oriented (performance) is a highly complex task. We define a new high-availability property for a VM, when a VM is marked as k-resilient, as long as there are up to k host failures, it should be guaranteed that it can be relocated to a non-failed host without relocating other VMs. Together with Hardware Predictive Failure Analysis and live migration, which enable VMs to be evacuated from a host before it fails, this property allows the continuous running of VMs on the cluster despite host failures. The complexity of the constraints associated with k-resiliency, which are naturally expressed by Second Order logic statements, prevented their integration into the placement computation until now. We present a novel algorithm which enables this integration by transforming the k-resiliency constraints to rules consumable by a generic Constraint Programming engine, prove that it guarantees the required resiliency and describe the implementation. We provide some preliminary results and compare our high availability support with naive solutions.
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paper_title: Particle Swarm Optimization for Energy-Aware Virtual Machine Placement Optimization in Virtualized Data Centers

paper_content:

A critical research issue is to lower the energy consumption of a virtualized data center by means of virtual machine placement optimization while satisfying the resource requirements of the cloud services. In this paper, we focus on different existing schemes and on the energy-aware virtual machine placement optimization problem of a heterogeneous virtualized data center. We attempt to explore a better alternative approach to minimizing the energy consumption, and we observe that particle swarm optimization (PSO) has considerable potential. However, the PSO must be improved to solve an optimization problem. The improvement includes redefining the parameters and operators of the PSO, adopting an energy-aware local fitness first strategy and designing a novel coding scheme. Using the improved PSO, an optimal virtual machine replacement scheme with the lowest energy consumption can be found. Experimental results indicate that our approach significantly outperforms other approaches, and can lessen 13%-23% energy consumption in the context of this paper.
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paper_title: Intelligent virtual machine placement for cost efficiency in geo-distributed cloud systems

paper_content:

An important challenge of running large-scale cloud services in a geo-distributed cloud system is to minimize the overall operating cost. The operating cost of such a system includes two major components: electricity cost and wide-area-network (WAN) communication cost. While the WAN communication cost is minimized when all virtual machines (VMs) are placed in one datacenter, the high workload at one location requires extra power for cooling facility and results in worse power usage effectiveness (PUE). In this paper, we develop a model to capture the intrinsic trade-off between electricity and WAN communication costs, and formulate the optimal VM placement problem, which is NP-hard due to its binary and quadratic nature. While exhaustive search is not feasible for large-scale scenarios, heuristics which only minimize one of the two cost terms yield less optimized results. We propose a cost-aware two-phase metaheuristic algorithm, Cut-and-Search, that approximates the best trade-off point between the two cost terms. We evaluate Cut-and-Search by simulating it over multiple cloud service patterns. The results show that the operating cost has great potential of improvement via optimal VM placement. Cut-and-Search achieves a highly optimized trade-off point within reasonable computation time, and outperforms random placement by 50%, and the partial-optimizing heuristics by 10-20%.
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paper_title: Energy-aware virtual machine placement in data centers

paper_content:

The energy efficiency of modern data centers has become a practical concern and has attracted significant attention in recent years. In contract to existing solutions that primarily focuses on only one specific aspect of management to reduce energy consumption, this paper explores the balance between server energy consumption and network energy consumption to present an energy-aware joint virtual machine (VM) placement. Given the definition of VM placement fairness, the basic algorithm of VM placement which fulfills server energy consumption constraints is conducted. Then, we further formulate the VM placement as an optimization problem which considers application dependencies to reduce network energy consumption. We design a joint algorithm that efficiently solves the VM placement problem for very large problem sizes. Using simulations, we conduct a comparative analysis on the impact of the data center architectures, server constraints and application dependencies on the potential performance gain of energy-aware VM placement. Compared to existing generic methods, we show a significant performance improvement such as efficiently reducing the number of physical machines used to save server energy consumption, decreasing the communication distance between VMs to obtain data center network energy consumption efficiency, improving scalability of data centers.
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paper_title: A network-aware virtual machine placement algorithm in mobile cloud computing environment

paper_content:

By taking into consideration network features of a wireless mesh network environment where mobile cloud computing (MCC) system is deployed, this paper proposes an effective virtual machine (VM) placement algorithm. This algorithm tends to place a newly created VM for a cloud service to a cloud server, which itself is also a mobile device, that occurs lowest network delay when accessing to application data stored in wireline networks. The aim is to reduce cloud service response time as much as possible in order to deliver better quality of experience to cloud end users over more noisy and less robust wireless networks. In addition, the proposed deployment of cloud storage and cloud computational resources onto wireless networks is also a contribution of the paper. The simulation results have shown the effectiveness and the efficiency of the proposed VM placement algorithms both in terms of cloud service response time.
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paper_title: Migration-based virtual machine placement in cloud systems

paper_content:

Cloud computing is an emerging technology that greatly shapes our lives, where users run jobs on virtual machines (VMs) on physical machines (PMs) provided by a cloud provider, saving the investment in upfront infrastructures. Due to the heterogeneity of various jobs, different VMs on the same PM can have different job completion times. Meanwhile, the PMs are also heterogeneous. Therefore, different VM placements have different job completion times. Our objective is to minimize the total job completion time of the input VM requests through a reasonable VM placement schedule. This problem is NP-hard, since it can be reduced to a knapsack problem. We propose an off-line VM placement method through emulated VM migration, while the on-line VM placement is solved by a real VM migration process. The migration algorithm is a heuristic approach, where we place the VM to its best PM directly, as long as it has enough capacity. Otherwise, if the migration constraint is satisfied, we migrate another VM from this PM to accommodate the new VM. Furthermore, we study a hybrid scheme where a batch is employed to accept upcoming VMs for the on-line scenario. Evaluation results prove the high efficiency of our algorithms.
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paper_title: An Energy Efficient Virtual Machine Placement Algorithm with Balanced Resource Utilization

paper_content:

Virtualization is a key technology for resource sharing in IaaS/PaaS cloud infrastructures. One primary issue in virtualization is the virtual machine placement (VMP) problem, which is to choose proper physical machine (PM) to deploy virtual machines (VMs) in runtime. In this paper, we study the VMP problem with the goal of minimizing the total energy consumption. We first present a multi-dimensional space partition model to characterize the resource usage states of PMs. Based on this model, we then propose a virtual machine placement algorithm, which can balance the utilization of multi-dimensional resources, reduce the number of running PMs and thus lower down the energy consumption. We also evaluate our proposed balanced algorithm via extensive simulations. Simulation results show that our approach can save as much as 15% energy compared to the first fit algorithm over a long run.
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paper_title: Reducing electricity cost through virtual machine placement in high performance computing clouds

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In this paper, we first study the impact of load placement policies on cooling and maximum data center temperatures in cloud service providers that operate multiple geographically distributed data centers. Based on this study, we then propose dynamic load distribution policies that consider all electricity-related costs as well as transient cooling effects. Our evaluation studies the ability of different cooling strategies to handle load spikes, compares the behaviors of our dynamic cost-aware policies to cost-unaware and static policies, and explores the effects of many parameter settings. Among other interesting results, we demonstrate that (1) our policies can provide large cost savings, (2) load migration enables savings in many scenarios, and (3) all electricity-related costs must be considered at the same time for higher and consistent cost savings.
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paper_title: An energy-efficient approach for virtual machine placement in cloud based data centers

paper_content:

Cloud computing is a new technology which is proffering IT services based on pay-as-you-go model to consumers from everywhere in the world. The growing demand of Cloud infrastructure and modern computational requests like business, scientific and web applications result in large-scale data centers and lead to extra electrical energy consumption. High energy consumption causes high operational cost and also led to high carbon emission which is harmful for atmosphere. Hence, energy-efficient techniques are required to minimize the negative effects of Cloud computing on the environment. Virtual machines (VMs) migration, dynamic consolidation in the virtualized data centers in cloud environments and switching idle physical machines off could yield reduce energy consumption; hence, one of the important issues for energy efficiency in virtualized cloud environments is how to place new VMs or selected VMs for migration across the hosts. In this paper we propose an energy-efficient approach based on Minimum Correlation Coefficient (MCC) method for virtual machine placement in cloud based, virtualized data centers. The proposed approach regards both Service Level Agreement (SLA) and low energy consumption and tries to make a trade-off between these two concerns using fuzzy Analytic Hierarchy Process (AHP). We evaluate our approach using Cloudsim toolkit as a modern cloud computing environment simulator. The evaluation shows that our proposed method offers a suitable trade-off between power efficiency and SLA violation reduction in cloud data centers.
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paper_title: Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement

paper_content:

The scalability of modern data centers has become a practical concern and has attracted significant attention in recent years. In contrast to existing solutions that require changes in the network architecture and the routing protocols, this paper proposes using traffic-aware virtual machine (VM) placement to improve the network scalability. By optimizing the placement of VMs on host machines, traffic patterns among VMs can be better aligned with the communication distance between them, e.g. VMs with large mutual bandwidth usage are assigned to host machines in close proximity. We formulate the VM placement as an optimization problem and prove its hardness. We design a two-tier approximate algorithm that efficiently solves the VM placement problem for very large problem sizes. Given the significant difference in the traffic patterns seen in current data centers and the structural differences of the recently proposed data center architectures, we further conduct a comparative analysis on the impact of the traffic patterns and the network architectures on the potential performance gain of traffic-aware VM placement. We use traffic traces collected from production data centers to evaluate our proposed VM placement algorithm, and we show a significant performance improvement compared to existing general methods that do not take advantage of traffic patterns and data center network characteristics.
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paper_title: Joint VM placement and routing for data center traffic engineering

paper_content:

Today's data centers need efficient traffic management to improve resource utilization in their networks. In this work, we study a joint tenant (e.g., server or virtual machine) placement and routing problem to minimize traffic costs. These two complementary degrees of freedom—placement and routing—are mutually-dependent, however, are often optimized separately in today's data centers. Leveraging and expanding the technique of Markov approximation, we propose an efficient online algorithm in a dynamic environment under changing traffic loads. The algorithm requires a very small number of virtual machine migrations and is easy to implement in practice. Performance evaluation that employs the real data center traffic traces under a spectrum of elephant and mice flows, demonstrates a consistent and significant improvement over the benchmark achieved by common heuristics.
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paper_title: Definition of Security Metrics for the Cloud Computing and Security-Aware Virtual Machine Placement Algorithms

paper_content:

Nowadays, Cloud Computing is becoming a key factor in computer science. Besides the great benefits it brought to the information technology and to the economy, Cloud Computing shows some weakness when looking at the security. An IaaS client should be able to specify its security requirements. But the lack of a system of security metrics leads to the incapability of quantifying the security level of a client deployment in a Cloud. Therefore, we propose a system of security metrics specific to the Cloud Computing and use it to develop virtual machines placement algorithms.
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paper_title: VMPlanner : Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers

paper_content:

In recent years, the power costs of cloud data centers have become a practical concern and have attracted significant attention from both industry and academia. Most of the early works on data center energy efficiency have focused on the biggest power consumers (i.e., computer servers and cooling systems), yet without taking the networking part into consideration. However, recent studies have revealed that the network elements consume 10-20% of the total power in the data center, which poses a great challenge to effectively reducing network power cost without adversely affecting overall network performance. Based on the analysis on topology characteristics and traffic patterns of data centers, this paper presents a novel approach, called VMPlanner, for network power reduction in the virtualization-based data centers. The basic idea of VMPlanner is to optimize both virtual machine placement and traffic flow routing so as to turn off as many unneeded network elements as possible for power saving. We formulate the optimization problem, analyze its hardness, and solve it by designing VMPlanner as a stepwise optimization approach with three approximation algorithms. VMPlanner is implemented and evaluated in a simulated environment with traffic traces collected from a data center test-bed, and the experiment results illustrate the efficacy and efficiency of this approach.
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paper_title: On Theory of VM Placement: Anomalies in Existing Methodologies and Their Mitigation Using a Novel Vector Based Approach

paper_content:

In this paper, we present the methodologies used in existing literature for Virtual Machine (VM) placement, load balancing and server consolidation in a data center environment. While the methodologies may seem fine on the surface, certain drawbacks and anomalies can be uncovered when they are analyzed deeper. We point out those anomalies and draw backs in the existing literature and explain what are the root causes of such anomalies. Then we propose a novel methodology based on vector arithmetic which not only addresses those anomalies but also leads to some interesting theories and algorithms to tackle the above mentioned three functionalities required in managing resources of data centers. We believe that with a strong mathematical base, our methodology has the potential to become the foundation of future models and algorithms in this research area.
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paper_title: Towards Profitable Virtual Machine Placement in the Data Center

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Motivated by the limit on the power usage effectiveness (PUE) of the data centers, the potential benefit of the consolidation, and the impetus of achieving maximum return on investment (ROI) on the cloud computing market, we investigate VM placement in the data center, formulate a multi-level generalized assignment problem (MGAP) for maximizing the profit under the service level agreement and the power budget constraint based on the model of a virtualized data center, and solve it with a first-fit heuristic. Numerical simulations show that the first-fit heuristic is effective in solving the large-scale instances of the MGAP with the sampled simulation setups.
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paper_title: Multiobjective Virtual Machine Placement in Cloud Environment

paper_content:

In this paper, the virtual machine placement problem is formulated as a multi-objective optimization problem. The objectives are maximizing profit, maximizing load balancing and minimizing recourse wastage. Results of Genetic algorithms, Non-dominated Sorting Genetic Algorithm and Non-dominated Sorting Genetic Algorithm-II are compared with common solution representation, penalty and benefit values. All the three algorithms reported good solutions whereas GA and NSGA are subjected to premature convergence and duplicate solutions. NSGA-II gives good and diversified range of solutions.
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paper_title: SLA guaranteed virtual machine consolidation for computing clouds

paper_content:

One of the most attractive features that computing clouds can offer is that the data center maintenance cost can be reduced by consolidating the workload generated by the virtual machines (VMs) into a few powerful physical servers. In this paper, the problem of maximally consolidating heterogeneous virtual machines into servers while protecting the service level agreement of each virtual machine is investigated. A generic and robust VM workload consolidation framework is proposed. The proposed framework achieves several attractive properties like low maintenance cost, low algorithm complexity, adjustable resource provisioning aggressiveness, and good scalability.
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paper_title: Prevent VM Migration in Virtualized Clusters via Deadline Driven Placement Policy

paper_content:

VM consolidation has been shown as a promising technique for saving energy costs of a data center. It relies on VM migration to move user applications or jobs onto fewer numbers of physical servers during off peak hour. However, VM migration is a costly operation that could cause several concerns, such as performance degradation and system instability. Most existing works were proposed to minimize the migration cost for dynamic consolidation which migrates VM at the runtime when SLA violation or resource under-utilization is detected. In contrast, this paper aims to proactively prevent VM migration for semi-static VM consolidation by proposing a deadline driven VM placement strategy based on the awareness of the server turn-off time and job execution time. We evaluate our approach using a real HPC cluster trace as well as a set of synthetic generated workloads. The results show our approach can significantly reduce the number of migrations by 70% on the real trace. We also demonstrate that our approach can be resilient to different workload patterns by achieving consistent improvement around 50% over all the synthetic workloads.
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paper_title: A Two Stage Heuristic Algorithm for Solving the Server Consolidation Problem with Item-Item and Bin-Item Incompatibility Constraints

paper_content:

The problem of server sprawl is common in data centers of most business organizations. It is most often the case that an application is run on dedicated servers. This leads to situations where organizations end up having numerous servers that remain under-utilized most of the times. The servers, in such scenarios, are allocated more resources (disk, cpu and memory) than are justified by their present workloads. Consolidating multiple underutilized servers into a fewer number of non-dedicated servers that can host multiple applications is an effective tool for businesses to enhance their returns on investment. The problem can be modeled as a variant of the bin packing problem where items to be packed are the servers being consolidated and bins are the target servers. The sizes of the servers/items being packed are resource utilizations which are obtained from the performance trace data. Here we describe a novel two stage heuristic algorithm for taking care of the "bin-item" incompatibility constraints that are inherent in any server consolidation problem. The model is able to solve extremely large instances of problem in a reasonable amount of time.
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paper_title: Virtual machine placement for predictable and time-constrained peak loads

paper_content:

We present an approach to optimal virtual machine placement within datacenters for predicable and time-constrained load peaks. A method for optimal load balancing is developed, based on binary integer programming. For tradeoffs between quality of solution and computation time, we also introduce methods to pre-process the optimization problem before solving it. Upper bound based optimizations are used to reduce the time required to compute a final solution, enabling larger problems to be solved. For further scalability, we also present three approximation algorithms, based on heuristics and/or greedy formulations. The proposed algorithms are evaluated through simulations based on synthetic data sets. The evaluation suggests that our algorithms are feasible, and that these can be combined to achieve desired tradeoffs between quality of solution and execution time.
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paper_title: On Modelling Virtual Machine Consolidation to Pseudo-Boolean Constraints

paper_content:

Cloud Computing is a new paradigm of distributed computing that offers virtualized resources and services over the Internet. To offer Infrastructure-as-a-Service (IaaS) many Cloud providers uses a large data center which usage ranges 5% to 10% of capacity in average. In order to improve Cloud data center management and resources usage a Virtual Machine (VM) consolidation technique can be applied to increase workloads and save energy. Using VM consolidation, we introduce an artificial intelligence consolidation based in Pseudo-Boolean (PB) Constraints to find a optimal consolidation. To evaluate our PB consolidation approach we used the DInf-UFPR and Google Cluster scenario and the formulas are solved with two state-of-the-art solvers.
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paper_title: Time-Aware VM-Placement and Routing with Bandwidth Guarantees in Green Cloud Data Centers

paper_content:

Variation in network performance due to the shared resources is a key obstacle for cloud adoption. Thus, the success of cloud providers to attract more tenants depends on their ability to provide bandwidth guarantees. Power efficiency in data centers has become critically important for supporting larger number of tenants. In this paper, we address the problem of time-aware VM-placement and routing (TVPR), where each tenant requests for a specified amount of server resources (VMs) and network resource (bandwidth) for a given duration. The TVPR problem allocates the required resources for as many tenants as possible by finding the right set of servers to map their VMs and routing their traffic so as to minimize the total power consumption. We propose a multi-component utilization-based power model to determine the total power consumption of a data center according to the resource utilization of the components (servers and switches). We then develop a mixed integer linear programming (MILP) optimization problem formulation based on the proposed power model and prove it to be N P-complete. Since the TVPR problem is computationally prohibitive, we develop a fast and scalable heuristic algorithm. To demonstrate the efficiency of our proposed algorithm, we compare its performance with the numerical results obtained by solving the MILP problem using CPLEX, for a small data center. We then demonstrate the effectiveness of the proposed algorithm in terms of power consumption and acceptance ratio for large data centers through simulation results.
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paper_title: Evolutionary Algorithms for Solving Multi-Objective Problems

paper_content:

List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.
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paper_title: Online traffic-aware virtual machine placement in data center networks

paper_content:

Data centers rely on virtualization to provide different services over a shared infrastructure. The placement of the different services and tasks in the physical machines is crucial for the performance of the whole system. A misplaced service can overload some network links, lead to congestion, or even connection disruptions. On the other hand, virtual machine migration allows reallocating services and changing the traffic matrix, leading to more efficient use of bandwidth. In this paper, we propose a Virtual Machine Placement (VMP) algorithm to (re)allocate virtual machines in the data center servers, based on the current traffic matrix, CPU, and memory usage. Analyzing the formation of community patterns in terms of traffic using graph theory, we are able to find virtual machines that are correlated because they exchange high amount of data. Those virtual machines are aggregated and allocated to servers as close as possible to each other, reducing traffic congestion. Our simulation results show that VMP was able to improve the traffic distribution. In some specific cases we were able to reduce 80% of the core traffic, concentrating it at the edge of the network.
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paper_title: A simulated annealing algorithm for energy efficient virtual machine placement

paper_content:

Improving energy efficiency has become increasingly important in data centers in recent years to reduce the rapidly growing tremendous amounts of electricity consumption. The power dissipation of the physical servers is the root cause of power usage of other systems, such as cooling systems. Many efforts have been made to make data centers more energy efficient. One of them is to minimize the total power consumption of these servers in a data center through virtual machine consolidation, which is implemented by virtual machine placement. The placement problem is often modeled as a bin packing problem. Due to the NP-hard nature of the problem, heuristic solutions such as First Fit and Best Fit algorithms have been often used and have generally good results. However, their performance leaves room for further improvement. In this paper we propose a Simulated Annealing based algorithm, which aims at further improvement from any feasible placement. This is the first published attempt of using SA to solve the VM placement problem to optimize the power consumption. Experimental results show that this SA algorithm can generate better results, saving up to 25 percentage more energy than First Fit Decreasing in an acceptable time frame.
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paper_title: Virtual Machine Placement Optimization Supporting Performance SLAs

paper_content:

Cloud computing model separates usage from ownership in terms of control on resource provisioning. Resources in the cloud are projected as a service and are realized using various service models like IaaS, PaaS and SaaS. In IaaS model, end users get to use a VM whose capacity they can specify but not the placement on a specific host or with which other VMs it can be co-hosted. Typically, the placement decisions happen based on the goals like minimizing the number of physical hosts to support a given set of VMs by satisfying each VMs capacity requirement. However, the role of the VMM usage to support I/O specific workloads inside a VM can make this capacity requirement incomplete. I/O workloads inside VMs require substantial VMM CPU cycles to support their performance. As a result, placement algorithms need to include the VMM's usage on a per VM basis. Secondly, cloud centers encounter situations wherein change in existing VM's capacity or launching of new VMs need to be considered during different placement intervals. Usually, this change is handled by migrating existing VMs to meet the goal of optimal placement. We argue that VM migration is not a trivial task and does include loss of performance during migration. We quantify this migration overhead based on the VM's workload type and include the same in placement problem. One of the goals of the placement algorithm is to reduce the VM's migration prospects, thereby reducing chances of performance loss during migration. This paper evaluates the existing ILP and First Fit Decreasing (FFD) algorithms to consider these constraints to arrive at placement decisions. We observe that ILP algorithm yields optimal results but needs long computing time even with parallel version. However, FFD heuristics are much faster and scalable algorithms that generate a sub-optimal solution, as compared to ILP, but in time-scales that are useful in real-time decision making. We also observe that including VM migration overheads in the placement algorithm results in a marginal increase in the number of physical hosts but a significant, of about 84 percent reduction in VM migration.
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paper_title: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing

paper_content:

Virtual machine placement is a process of mapping virtual machines to physical machines. The optimal placement is important for improving power efficiency and resource utilization in a cloud computing environment. In this paper, we propose a multi-objective ant colony system algorithm for the virtual machine placement problem. The goal is to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption. The proposed algorithm is tested with some instances from the literature. Its solution performance is compared to that of an existing multi-objective genetic algorithm and two single-objective algorithms, a well-known bin-packing algorithm and a max-min ant system (MMAS) algorithm. The results show that the proposed algorithm is more efficient and effective than the methods we compared it to.
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paper_title: Design and Implementation of a Multi-objective Optimization Mechanism for Virtual Machine Placement in Cloud Computing Data Center

paper_content:

Cloud computing is becoming a popular way of supplying and using computing resources. A cloud-computing data center is equipped with a large number of physical resources and must manage an even larger number of virtual machines (VMs). The center’s VM placement strategy affects the utilization of physical resources, and consequently, it influences operational costs. Our goal is to develop a multi-objective optimization mechanism for VM placement that satisfies various constraints and results in the lowest operational cost. The number of possible combinations of VMs and hosts can be extremely large. For the mechanism to be practical, the number of possible combinations must be reduced. We reduced computational overheads by classifying VM hosts into a relatively small number of equivalent sets. Simulation results show that expected operational costs can be significantly reduced by applying the proposed mechanism.
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paper_title: An Availability-Aware Virtual Machine Placement Approach for Dynamic Scaling of Cloud Applications

paper_content:

Cloud computing promises customers the on-demand ability to dynamically provision virtualization resources in face of workload variations. Most existing scaling approaches addressed this problem by allocating application to a certain amount of cloud resources. However, the problem of the availability of application influenced by VM-based physical locations during resource scaling process is a serious challenge due to dynamic complex workload and has not been widely discussed yet. In this paper, we present a novel availability-based computing model to describe availability attribute of one application in the hierarchical structured cloud. Moreover, we propose an availability-aware policy by performing both vertical and horizontal scaling to explore how and where to allocate computing resource. Simulation results indicate that our model captured the availability of cloud applications properly and proposed scaling approach achieves the objectives of meeting availability demands and minimizing the total communication cost.
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paper_title: Improving Performance and Availability of Services Hosted on IaaS Clouds with Structural Constraint-Aware Virtual Machine Placement

paper_content:

The increasing popularity of modern virtualization-based datacenters continues to motivate both industry and academia to provide answers to a large variety of new and challenging questions. In this paper we aim to answer focusing on one such question: how to improve performance and availability of services hosted on IaaS clouds. Our system, structural constraint-aware virtual machine placement (SCAVP), supports three types of constraints: demand, communication and availability. We formulate SCAVP as an optimization problem and show its hardness. We design a hierarchical placement approach with four approximation algorithms that efficiently solves the SCAVP problem for large problem sizes. We provide a formal model for the application (to better understand structural constraints) and the datacenter (to effectively capture capabilities), and use the two models as inputs to the placement problem. We evaluate SCAVP in a simulated environment to illustrate the efficiency and importance of the proposed approach.
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paper_title: An energy-aware heuristic framework for virtual machine consolidation in Cloud computing

paper_content:

Virtual machine (VM) consolidation in Cloud computing provides a great opportunity for energy saving. However, the obligation of providing suitable quality of service to end users leads to the necessity in dealing with energy-performance tradeoff. In this paper, we propose a redesigned energy-aware heuristic framework for VM consolidation to achieve a better energy-performance tradeoff. There are two main contributions in the framework: (1) establish a service level agreement (SLA) violation decision algorithm to decide whether a host is overload with SLA violation; (2) minimum power and maximum utilization policy is then proposed to improve the Minimum Power policy in previous work. Finally, we have evaluated our framework through simulation on large-scale experiments driven by workload traces from more than a thousand VMs, and the results show that our framework outperforms previous work. Specifically, it guarantees 21---34 % decrease in energy consumption, 84---92 % decrease in SLA violation, 87---94 % decrease in energy-performance metric, and 63 % decrease in execution time. And we further discuss why the redesigned framework outperforms the previous design.
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paper_title: Optimal virtual machine placement across multiple cloud providers

paper_content:

Cloud computing provides users an efficient way to dynamically allocate computing resources to meet demands. Cloud providers can offer users two payment plans, i.e., reservation and on-demand plans for resource provisioning. Price of resources in reservation plan is generally cheaper than that in on-demand plan. However, since the reservation plan has to be acquired in advance, it may not fully meet future demands in which the on-demand plan can be used to guarantee the availability to the user. In this paper, we propose an optimal virtual machine placement (OVMP) algorithm. This algorithm can minimize the cost spending in each plan for hosting virtual machines in a multiple cloud provider environment under future demand and price uncertainty. OVMP algorithm makes a decision based on the optimal solution of stochastic integer programming (SIP) to rent resources from cloud providers. The performance of OVMP algorithm is evaluated by numerical studies and simulation. The results clearly show that the proposed OVMP algorithm can minimize users' budgets. This algorithm can be applied to provision resources in emerging cloud computing environments.
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paper_title: VM Placement Strategies for Cloud Scenarios

paper_content:

The problem of Virtual Machine (VM) placement in a compute cloud infrastructure is well-studied in the literature. However, the majority of the existing works ignore the dynamic nature of the incoming stream of VM deployment requests that continuously arrive to the cloud provider infrastructure. In this paper we provide a practical model of cloud placement management under a stream of requests and present a novel technique called Backward Speculative Placement (BSP)that projects the past demand behavior of a VM to a candidate target host. We exploit the BSP technique in two algorithms, first for handling the stream of deployment requests, second in a periodic optimization, to handle the dynamic aspects of the demands. We show the benefits of our BSPtechnique by comparing the results on a simulation period with a strategy of choosing an optimal placement at each time instant, produced by a generic MIP solver.
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paper_title: Redundant virtual machine placement for fault-tolerant consolidated server clusters

paper_content:

Consolidated server systems using server virtualization involves serious risks of host server failures that induce unexpected downs of all hosted virtual machines and applications. To protect applications requiring high-availability from unpredictable host server failures, redundant configuration using virtual machines can be an effective countermeasure. This paper presents a virtual machine placement method for establishing a redundant configuration against host server failures with less host servers. The proposed method estimates the requisite minimum number of virtual machines according to the performance requirements of application services and decides an optimum virtual machine placement so that minimum configurations survive at any k host server failures. The evaluation results clarify that the proposed method achieves requested fault-tolerance level with less number of hosting servers compared to the conventional N+M redundant configuration approach.
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paper_title: Elasticity-aware virtual machine placement for cloud datacenters

paper_content:

With the increasing popularity of cloud computing, the cloud datacenter suffers from both limited resources and the variation of users' requests. One important feature of cloud computing is on-demand scaling, enabling the fluctuation of one user's resource demand. However, amongst previous work concerning the virtual machine (VM) placement in datacenters, satisfying the VMs' requested resources of users is the primary objective, neglecting future demand variation. In this paper, we propose the concept of elasticity, referring to how well the datacenter can satisfy the growth of the input VMs resource demands under both the limitations of physical machines (PMs) capacities and links capacities. To consider both dimensions of the machine and bandwidth resources simultaneously, we propose our hierarchical VM placement algorithm. We also prove the optimality of our algorithm in a frequently used semi-homogeneous datacenter configuration. Furthermore, we study the heterogeneous datacenter configuration, favoring the characteristics of multi-tenant datacenters. Evaluation results validate the efficiency of our algorithm.
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paper_title: A Matrix Transformation Algorithm for Virtual Machine Placement in Cloud

paper_content:

The virtual machine (VM) placement is an important problem of cloud data center resource management. There have been many research efforts to address this problem. However, existing research is mainly concerned with single objective such as the number of the physical machine (PM) used, and few research consider the resource utilization rate of PM's different dimensions. In this paper, we present a two-objective optimization model such that the number of required PM is minimized and the resource utilization of occupied PM is optimum. We further provide an algorithm based on matrix transformation. In the algorithm, we define cluster matrix of VM request sequence and VM placement initial matrix to generate the optimum solution of VM placement optimization model. Finally, we compare our algorithm with the other VM placement strategy, the experimental results demonstrate the good performance of our algorithm.
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paper_title: Virtual Machine Placement for Improving Energy Efficiency and Network Performance in IaaS Cloud

paper_content:

Under the premise of ensuring application performance, how to place virtual machines (VMs) on physical machines (PMs) to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers in IaaS cloud. The existing VM placement schemes are mostly to reduce energy consumption by optimizing utilization of physical server or network elements, but the issue of aggressive consolidation of VM is ignored, which may lead to network performance degradation. To address the issue, this paper proposes a VM placement scheme based on a new two-stage heuristic algorithm to optimize network performance and to reduce energy consumption of physical servers and network elements, so as to finally achieve the tradeoff between energy efficiency and network performance. The simulations show that our solution achieves good results.
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paper_title: Energy-Saving Virtual Machine Placement in Cloud Data Centers

paper_content:

In cloud data centers, different mapping relationships between virtual machines (VMs) and physical machines (PMs) cause different resource utilization, therefore, how to place VMs on PMs to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers. The existing VM placement schemes are to optimize physical server resources utilization or network resources utilization, but few of them focuses on optimizing multiple resources utilization simultaneously. To address the issue, this paper proposes a VM placement scheme meeting multiple resource constraints, such as the physical server size (CPU, memory, storage, bandwidth, etc.) and network link capacity to improve resource utilization and reduce both the number of active physical servers and network elements so as to finally reduce energy consumption. Since VM placement problem is abstracted as a combination of bin packing problem and quadratic assignment problem, which is also known as a classic combinatorial optimization and NP-hard problem, we design a novel greedy algorithm by combining minimum cut with the best-fit, and the simulations show that our solution achieves better results.
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paper_title: Higher SLA satisfaction in datacenters with continuous VM placement constraints

paper_content:

In a virtualized datacenter, the Service Level Agreement for an application restricts the Virtual Machines (VMs) placement. An algorithm is in charge of maintaining a placement compatible with the stated constraints. Conventionally, when a placement algorithm computes a schedule of actions to re-arrange the VMs, the constraints ignore the intermediate states of the datacenter to only restrict the resulting placement. This situation may lead to temporary violations of the constraints. In this paper, we discuss the causes of these violations. We then advocate for continuous placement constraints to restrict also the actions schedule. We discuss why their development requires more attention and how the extensible placement algorithm BtrPlace can address this issue.
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paper_title: Energy-Efficient Virtual Machine Placement in Data Centers by Genetic Algorithm

paper_content:

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches to the virtual machine placement problem consider the energy consumption by physical machines in a data center only, but do not consider the energy consumption in communication network in the data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement in order to make the data center more energy-efficient. In this paper, we propose a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both the servers and the communication network in the data center. Experimental results show that the genetic algorithm performs well when tackling test problems of different kinds, and scales up well when the problem size increases.
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paper_title: Shadow-routing based dynamic algorithms for virtual machine placement in a network cloud

paper_content:

We consider a shadow routing based approach to the problem of real-time adaptive placement of virtual machines (VM) in large data centers (DC) within a network cloud. Such placement in particular has to respect vector packing constraints on the allocation of VMs to host physical machines (PM) within a DC, because each PM can potentially serve multiple VMs simultaneously. Shadow routing is attractive in that it allows a large variety of system objectives and/or constraints to be treated within a common framework (as long as the underlying optimization problem is convex). Perhaps even more attractive feature is that the corresponding algorithm is very simple to implement, it runs continuously, and adapts automatically to changes in the VM demand rates, changes in system parameters, etc., without the need to re-solve the underlying optimization problem “from scratch”. In this paper we focus on the minmax-DC-load problem. Namely, we propose a combined VM-toDC routing and VM-to-PM assignment algorithm, referred to as Shadow scheme, which minimizes the maximum of appropriately defined DC utilizations. We prove that the Shadow scheme is asymptotically optimal (as one of its parameters goes to 0). Simulation confirms good performance and high adaptivity of the algorithm. Favorable performance is also demonstrated in comparison with a baseline algorithm based on VMware implementation [7], [8]. We also propose a simplified - “more distributed” - version of the Shadow scheme, which performs almost as well in simulations.
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paper_title: Optimizing data access latencies in cloud systems by intelligent virtual machine placement

paper_content:

Many cloud applications are data intensive requiring the processing of large data sets and the MapReduce/Hadoop architecture has become the de facto processing framework for these applications. Large data sets are stored in data nodes in the cloud which are typically SAN or NAS devices. Cloud applications process these data sets using a large number of application virtual machines (VMs), with the total completion time being an important performance metric. There are many factors that affect the total completion time of the processing task such as the load on the individual servers, the task scheduling mechanism, communication and data access bottlenecks, etc. One dominating factor that affects completion times for data intensive applications is the access latencies from processing nodes to data nodes. Ideally, one would like to keep all data access local to minimize access latency but this is often not possible due to the size of the data sets, capacity constraints in processing nodes which constrain VMs from being placed in their ideal location and so on. When it is not possible to keep all data access local, one would like to optimize the placement of VMs so that the impact of data access latencies on completion times is minimized. We address this problem of optimized VM placement - given the location of the data sets, we need to determine the locations for placing the VMs so as to minimize data access latencies while satisfying system constraints. We present optimal algorithms for determining the VM locations satisfying various constraints and with objectives that capture natural tradeoffs between minimizing latencies and incurring bandwidth costs. We also consider the problem of incorporating inter-VM latency constraints. In this case, the associated location problem is NP-hard with no effective approximation within a factor of 2 - ϵ for any ϵ > 0. We discuss an effective heuristic for this case and evaluate by simulation the impact of the various tradeoffs in the optimization objectives.
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paper_title: A Mathematical Programming Approach for Server Consolidation Problems in Virtualized Data Centers

paper_content:

Today's data centers offer IT services mostly hosted on dedicated physical servers. Server virtualization provides a technical means for server consolidation. Thus, multiple virtual servers can be hosted on a single server. Server consolidation describes the process of combining the workloads of several different servers on a set of target servers. We focus on server consolidation with dozens or hundreds of servers, which can be regularly found in enterprise data centers. Cost saving is among the key drivers for such projects. This paper presents decision models to optimally allocate source servers to physical target servers while considering real-world constraints. Our central model is proven to be an NP-hard problem. Therefore, besides an exact solution method, a heuristic is presented to address large-scale server consolidation projects. In addition, a preprocessing method for server load data is introduced allowing for the consideration of quality-of-service levels. Extensive experiments were conducted based on a large set of server load data from a data center provider focusing on managerial concerns over what types of problems can be solved. Results show that, on average, server savings of 31 percent can be achieved only by taking cycles in the server workload into account.
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paper_title: A virtual machine consolidation framework for MapReduce enabled computing clouds

paper_content:

In nowadays computing clouds, it is of the cloud providers' economic interests to correctly consolidate the workload of the virtual machines (VMs) into the suitable physical servers in the cloud data center in order to minimize the total maintenance cost. However, during the consolidation process, sufficient protection should be provided to the service level agreement (SLA) of the VMs. In this paper, the VM consolidation problem for MapReduce enabled computing clouds has been investigated. In the MapReduce enabled computing clouds, MapReduce jobs are carried out by homogeneous MapReduce VM instances that have identical hardware resource. Two resource allocation schemes with corresponding SLA constraints for the MapReduce VMs and the non-MapReduce VMs are proposed. Based on these schemes, the VM consolidation problem is modeled as an integer nonlinear optimization problem and an efficient algorithm has been proposed to locate its solutions. The results show that better VM consolidation performance can be achieved by colocating MapReduce instances together with non-MapReduce instances in the same set of physical servers.
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paper_title: Reduce Energy Consumption through Virtual Machine Placement in Cloud Data Centre

paper_content:

In this paper, energy consumption in the data centre was studied where thousands of servers and other devices runs, energy are utilized to run the server and cooling the environment. Energy consumption can be reduced by switching off the idle server by means of migration of Virtual Machine from under-load Host. Load in cloud computing is maintained by migration of VM from the overloaded Host to a free Host or activate new Host. Based on this study, a reservation technique by using BIN packing was proposed in this paper with an overload detection algorithm. The proposed algorithm RBIN is experimented in 800 servers with 1024 Virtual Machines. From the experimental result, proposed method RBIN reduces energy in higher level.
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paper_title: CCAP: A Cache Contention-Aware Virtual Machine Placement Approach for HPC Cloud

paper_content:

Applications in High Performance Computing (HPC) cloud are characterized by large cache resource consumption due to large-scale inputs and intensive communications, which creates serious Shared Last Level cache (SLLC) performance bottleneck. Current system software stacks are not efficient in addressing this issue among virtual machines at the hypervisor level or the threads at the operating system level. In this paper, we investigate performance interference due to contention for SLLC in the HPC cloud. We employ an enhanced reuse distance analysis technique with an accelerated cyclic compression algorithm to identify application's cache interference intensity. Based on reuse distance analysis, we propose a practical Cache Contention-Aware virtual machine Placement approach (CCAP). CCAP dispatches virtual machines according to their cache interference intensities to avoid cache pollution and interference, thus alleviating negative effects of cache contention. We implement CCAP in the Xen hypervisor. Evaluation of NPB workload reveals that CCAP can improve performance of cache sensitive applications when they are co-scheduled with cache pollution programs. For a 2-workload system, it reduces execution time by 12 %, as well as cache miss rate by 13 %, while increasing throughput by 13 %, on average. Moreover, CCAP also improves the average performance of the cache pollution programs by 5 %. For a 4-workload system, CCAP brings more significant performance improvement to cache sensitive applications, an average increase of 20 %.
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paper_title: Workload characteristics-aware virtual machine consolidation algorithms

paper_content:

An energy conservation strategy must address two issues — placement of virtual machine images and workload characteristics of virtual machines. For performance reason most cloud systems copy a prototype image into the local disk of a physical machine before starting a virtual machine. If the physical machine that stores the image of a virtual machine is off-line, then we cannot run this virtual machine. The workload characteristics of virtual machines determine whether it is data-intensive or CPU-intensive. We assume that the system has a distributed file system therefore a physical machine can run any virtual machine even if it does not have the image. However, we observe that the performance of a data-intensive virtual machine running on a physical machine without its image could result in 60% performance loss compared with running the same virtual machine on a physical machine that has the virtual machine image. On the other hand, the performance of a CPU-intensive virtual machine is almost independent of whether the physical machine has the image or not. As a result, an energy conservation algorithm must consider the workload characteristic of a virtual machine when finding a physical machine to run it, especially for data-intensive virtual machines. This paper proposes a workload characteristics-aware virtual machine consolidation algorithms. We propose an approximation algorithm and two dynamic programmings to consolidate virtual machines and reduce the number of physical machines. We conduct experiments and compare the numbers of physical machines used by our approximation algorithm with the optimal number of physical machines found by our dynamic programming. The experiment results indicate that our approximation algorithm finds good solutions much faster than the dynamic programming.
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paper_title: An energy aware framework for virtual machine placement in cloud federated data centres

paper_content:

Data centres are powerful ICT facilities which constantly evolve in size, complexity, and power consumption. At the same time users' and operators' requirements become more and more complex. However, existing data centre frameworks do not typically take energy consumption into account as a key parameter of the data centre's configuration. To lower the power consumption while fulfilling performance requirements we propose a flexible and energy-aware framework for the (re)allocation of virtual machines in a data centre. The framework, being independent from the data centre management system, computes and enacts the best possible placement of virtual machines based on constraints expressed through service level agreements. The framework's flexibility is achieved by decoupling the expressed constraints from the algorithms using the Constraint Programming (CP) paradigm and programming language, basing ourselves on a cluster management library called Entropy. Finally, the experimental and simulation results demonstrate the effectiveness of this approach in achieving the pursued energy optimization goals.
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paper_title: A Relationship-Based VM Placement Framework of Cloud Environment

paper_content:

Managing computation resources in a cost-effective way has become the core competence for a Cloud provider to win over the market because of the "pay-as-you-go" business model. Therefore, VM placement has become more and more important in the research and practices of VM management by determining at what condition and on which physical server a VM should be placed so that the SLA can be guaranteed and servers' utilization can be improved. Much existing work simply formulates the above issue to be a bin-packing problem, which does not take the VM relationships into account. However, the relationship information can greatly impact the SLA of the Cloud system and the resource utilization. Therefore, in this paper, we propose a relationship-based VM placement framework, SmartCRS, to optimize the VM placement procedure. SmartCRS reveals the relationships between VMs automatically. Then by using such information and based on a constraint library, it gives a proper VM placement plan. Finally, the plan is carried out by SmartCRS automatically or by Cloud administrators manually to improve the server utilization and guarantee the required SLA. Two case studies are conducted to demonstrate the effectiveness and efficiency of the proposed framework at the end of this paper.
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paper_title: EQVMP: Energy-efficient and QoS-aware virtual machine placement for software defined datacenter networks

paper_content:

To provide effective and reliable services, cloud datacenters need parallel computing and virtualization techniques. This work presents an improved virtual machine (VM) placement mechanism, called Energy efficiency and Quality of Service (QoS) aware VM Placement (EQVMP) to overcome the problem of unbalanced traffic load in switching on and off VMs for the purpose of energy saving. EQVMP combines three key techniques: (1) hop reduction, (2) energy saving and (3) load balancing. Hop reduction can regroup VMs to lower the traffic load among them. Energy saving techniques aim at choosing the appropriate servers. The proposed load balancing updates VM placement periodically. Our experimental results show that the proposed scheme can lower energy consumption and maintain QoS. We propose an evaluation score [1] to assess VM placement in terms of energy, delay and throughput. Comparing to other existing placement policies, our proposed mechanism can enhance system throughput by 25% and can have better evaluation score.
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paper_title: Optimal update frequency model for physical machine state change and virtual machine placement in the cloud

paper_content:

Cloud computing is evolving into the default operational framework running modern data centers. Efficient data center operation is concerned with the total amount of energy consumed as well as assuring adequate resources are available to process all of the incoming work requests. Existing research has demonstrated several algorithms that can be used to determine the optimal number of resources required to service these requests. However, a key issue not addressed in these algorithms is determining the frequency of recalculating the number of required resources. Changing the required resources at a rate slower than the optimal update frequency results in lower energy efficiency due to the over allocation of resources. Changing the resources at a rate higher than the optimal frequency results in insufficient time for systems to change state, which results in SLA violations. In this paper, a stochastic optimization model is presented that determines the optimal update frequency for changing the states of the nodes of the cloud as well as determining the proper frequency for recalculating the maximum expected load, which improves the determination of the optimum number of resources required, therefore maximizes energy efficiency and minimizes SLA violations.
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paper_title: Network-aware virtual machine consolidation for large data centers

paper_content:

Resource management in modern data centers has become a challenging task due to the tremendous growth of data centers. In large virtual data centers, performance of applications is highly dependent on the communication bandwidth available among virtual machines. Traditional algorithms either do not consider network I/O details of the applications or are computationally intensive. We address the problem of identifying the virtual machine clusters based on the network traffic and placing them intelligently in order to improve the application performance and optimize the network usage in large data center. We propose a greedy consolidation algorithm that ensures the number of migrations is small and the placement decisions are fast, which makes it practical for large data centers. We evaluated our approach on real world traces from private and academic data centers, using simulation and compared the existing algorithms on various parameters like scheduling time, performance improvement and number of migrations. We observed a ~70% savings of the interconnect bandwidth and overall ~60% improvements in the applications performances. Also, these improvements were produced within a fraction of scheduling time and number of migrations.
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paper_title: Energy Efficient VM Placement Supported by Data Analytic Service

paper_content:

The popularity and commercial use of cloud computing has prompted an increased concern among cloud service providers for energy efficiency while still maintaining quality of service. One of the key techniques used for the efficient use of cloud server resources is virtual machine placement. This work introduces a precise VM placement algorithm that ensures energy efficiency and also prevents Service Level Agreement (SLA) violation. The mathematical model of the algorithm is supported by a sophisticated data analytic system implemented as a service. The precision of the algorithm is achieved by allowing each individual VM to build its own data model on demand over an appropriate time horizon. Thus the data model can reflect accurately the characteristics of resource usage of the VM. The algorithm can communicate synchronously or asynchronously with the data analytic service which is deployed as a cloud-based solution. In the experiments, several advanced data modelling and use forecasting techniques were evaluated. Results from simulation-based experiments show that the VM placement algorithm (supported by the data analytic service) can effectively reduce power consumption, the number of VM migrations, and prevent SLA violation, it also compares very favourably with other placement algorithms.
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paper_title: Evolutionary Optimal Virtual Machine Placement and Demand Forecaster for Cloud Computing

paper_content:

Cloud computing allows the users to efficiently and dynamically provision computing resources to meet their IT needs. Most cloud providers offer two types of payment plans to the user, i.e., reservation and on-demand. The reservation plan is typically cheaper than the on-demand plan but reservation plan has to be provisioned in advance. Reserving the resources would be straightforward if the actual computing demand (e.g., job processing) is known in advance. However, in reality, the actual computing demand can be observed only at the point of actual usage. Therefore, it is difficult to reserve the correct amount of resources during the reservation to meet the computing demands of the users. In this paper, we propose an evolutionary optimal virtual machine placement (EOVMP) algorithm with a demand forecaster. First, a demand forecaster predicts the computing demand. Then, EOVMP uses this predicted demand to allocate the virtual machines using reservation and on-demand plans for job processing. The performance of the proposed schemes is evaluated by simulations and numerical studies. The evaluation result shows that the EOVMP algorithm can provide the solution close to the optimal solution of stochastic integer programming (SIP) and the prediction of the demand forecaster is of reasonable accuracy.
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paper_title: Maximum migration time guarantees in dynamic server consolidation for virtualized data centers

paper_content:

Server consolidation is a vital mechanism in modern data centers in order to minimize expenses with infrastructure. In most cases, server consolidation may require migrating virtual machines between different physical servers. Although the downtime of live-migration is negligible, the amount of time to migrate all virtual machines can be substantial, delaying the completion of the consolidation process. This paper proposes a new server consolidation algorithm, which guarantees that migrations are completed in a given maximum time. The migration time is estimated using the max-min fairness model, in order to consider the competition of migration flows for the network infrastructure. The algorithm was simulated using a real workload and shows a good consolidation ratio in comparison to other algorithms, while also guaranteeing a maximum migration time.
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paper_title: A Stable Network-Aware VM Placement for Cloud Systems

paper_content:

Virtual Machine (VM) placement has to carefully consider the aggregated resource consumption of co-located VMs in order to obey service level agreements at lower possible cost. In this paper, we focus on satisfying the traffic demands of the VMs in addition to CPU and memory requirements. This is a much more complex problem both due to its quadratic nature (being the communication between a pair of VMs) and since it involves many factors beyond the physical host, like the network topologies and the routing scheme. Moreover, traffic patterns may vary over time and predicting the resulting effect on the actual available bandwidth between hosts within the data center is extremely difficult. We address this problem by trying to allocate a placement that not only satisfies the predicted communication demand but is also resilient to demand time-variations. This gives rise to a new optimization problem that we call the Min Cut Ratio-aware VM Placement (MCRVMP). The general MCRVMP problem is NP-Hard, hence, we introduce several heuristics to solve it in reasonable time. We present extensive experimental results, associated with both placement computation and run-time performance under time-varying traffic demands, to show that our heuristics provide good results (compared to the optimal solution) for medium size data centers.
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paper_title: PBFVMC: A New Pseudo-Boolean Formulation to Virtual-Machine Consolidation

paper_content:

Over the course of the last decade, there have been several improvements in the performance of Boolean Satisfiability (SAT), Integer Linear Programming (ILP) and Pseudo-Boolean Optimization (PBO) solvers. These improvements have encouraged the applications of SAT, ILP and PBO techniques in modeling complex engineering problems. One such problem is the Virtual Machine Consolidation. The Virtual Machine Consolidation problem consists in placing a set of virtual machines in a set of hardware in a way to increase workload on hardware where they can operate more energy-efficient. This paper proposes an improved PBO formulation of the Virtual Machine Consolidation problem, PBFVMC. The improved formulation and enhancements are built on top of a previous work and a new set of constraints is created and rationalized to work more friendly with current PBO solvers. It is observed that this new formulation goes a step ahead and more problems can now be solved.
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paper_title: QoS-Aware VM Placement in Multi-domain Service Level Agreements Scenarios

paper_content:

Virtualization technologies of Infrastructure-as-a- Service enable the live migration of running Virtual Machines (VMs) to achieve load balancing, fault-tolerance and hardware consolidation in data centers. However, the downtime/service unavailability due to live migration may be substantial with relevance to the customers' expectations on responsiveness, as the latter are declared in established Service Level Agreements (SLAs). Moreover, it may cause significant (potentially exponential) SLA violation penalties to its associated higher- level domains (Platform-as-a-Service and Software-as-a-Service). Therefore, VM live migration should be managed carefully. In this paper, we present the OpenStack version of the Generic SLA Manager, alongside its strategies for VM selection and allocation during live migration of VMs. We simulate a use case where IaaS (OpenStack-SLAM) and PaaS (OpenShift) are combined, and assess performance and efficiency of the aforementioned VM placement strategies, when a multi-domain SLA pricing & penalty model is involved. We find that our proposal is efficient in managing trade-offs between the operational objectives of service providers (including financial considerations) and the customers' expected QoS requirements.
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paper_title: Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center

paper_content:

Powerful data centers are the essential supporting infrastructure for mobile, ubiquitous, and cognitive computing, which are the most popular computing paradigms to utilize all kinds of physical resources and provide various services. To ensure the high quality of services, the performance and cost of a data center is a critical factor. In this paper, we investigate the issue of increasing the resource utilization of data centers to improve their performance and lower the cost. It is an efficient way to increase resource utilization via resource sharing. Technically, server virtualization provides the opportunity to share resources in data centers. However, it also introduces other problems, the primary problem being virtual machine placement (VMP), which is to choose a proper physical machine (PM) to deploy virtual machines (VMs) in runtime. We study the virtual machine placement problem with the target of minimizing the total energy consumption by the running of PMs, which is also an indication of resource utilization and the cost of a data center. Due to the multiple dimensionality of physical resources, there always exists a waste of resources, which results from the imbalanced use of multi-dimensional resources. To characterize the multi-dimensional resource usage states of PMs, we present a multi-dimensional space partition model. Based on this model, we then propose a virtual machine placement algorithm EAGLE, which can balance the utilization of multi-dimensional resources, reduce the number of running PMs, and thus lower the energy consumption. We also evaluate our proposed balanced algorithm EAGLE via extensive simulations and experiments on real traces. Experimental results show, over the long run, that EAGLE can save as much as 15% more energy than the first fit algorithm.
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paper_title: A Network-aware Virtual Machine Placement and Migration Approach in Cloud Computing

paper_content:

Cloud computing represents a major step up in computing whereby shared computation resources are provided on demand. In such a scenario, applications and data thereof can be hosted by various networked virtual machines (VMs). As applications, especially data-intensive applications, often need to communicate with data frequently, the network I/O performance would affect the overall application performance significantly. Therefore, placement of virtual machines which host an application and migration of these virtual machines while the unexpected network latency or congestion occurs is critical to achieve and maintain the application performance. To address these issues, this paper proposes a virtual machine placement and migration approach to minimizing the data transfer time consumption. Our simulation studies suggest that the proposed approach is effective in optimizing the data transfer between the virtual machine and data, thus helping optimize the overall application performance.
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paper_title: Power-Efficient Virtual Machine Placement and Migration in Data Centers

paper_content:

In this paper, we propose a power-efficient solution for virtual machine placement and migration in a fat tree data center network. This solution reduces power consumption as well as job delay by aggregating virtual machines to a few hyper visors and migrating communicating parties to close locations. In this work, we consider OpenFlow as the implementation protocol. In an OpenFlow environment, a centralized controller oversees job loads, virtual machine requirements and hardware availability. Given observation of such global knowledge, the OpenFlow controller can schedule jobs and distribute virtual machines accordingly. As jobs change and flows shift, the OpenFlow controller dynamically adjusts virtual machine assignments by aggregating virtual machines to close locations in order to save energy. With this placement and migration proposal, more jobs can operate concurrently with close sources and destinations of flows, thus both job and flow delay can be reduced.
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paper_title: Hierarchical Virtual Machine Consolidation in a Cloud Computing System

paper_content:

Improving the energy efficiency of cloud computing systems has become an important issue because the electric energy bill for 24/7 operation of these systems can be quite large. The focus of this paper is on the virtual machine (VM) consolidation in a cloud computing system as a way of lowering daily energy consumption of the system. In contrast to the existing works that assume resource demands of VMs are known and given as scalar variables, this paper treats these demands as random variables with known means and standard deviations. These random variables may be correlated with one another, and there are several kinds of resources which can be performance bottlenecks. Therefore, both the correlation and multiple resource type should be considered. The VM consolidation problem is then formulated as a multi-capacity stochastic bin packing problem. This problem is NP-hard, so we propose a heuristic method to solve the problem efficiently. The simulation results show that, in spite of its simplicity and scalability, the proposed method produces high quality solutions.
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paper_title: Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments

paper_content:

Server consolidation using virtualization technology has become increasingly important for improving data center efficiency. It enables one physical server to host multiple independent virtual machines (VMs), and the transparent movement of workloads from one server to another. Fine-grained virtual machine resource allocation and reallocation are possible in order to meet the performance targets of applications running on virtual machines. On the other hand, these capabilities create demands on system management, especially for large-scale data centers. In this paper, a two-level control system is proposed to manage the mappings of workloads to VMs and VMs to physical resources. The focus is on the VM placement problem which is posed as a multi-objective optimization problem of simultaneously minimizing total resource wastage, power consumption and thermal dissipation costs. An improved genetic algorithm with fuzzy multi-objective evaluation is proposed for efficiently searching the large solution space and conveniently combining possibly conflicting objectives. The simulation-based evaluation using power-consumption and thermal-dissipation models based on profiling of a Blade Center, demonstrates the good performance, scalability and robustness of our proposed approach. Compared with four well-known bin-packing algorithms and two single-objective approaches, the solutions obtained from our approach seek good balance among the conflicting objectives while others cannot.
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paper_title: Profiling Applications for Virtual Machine Placement in Clouds

paper_content:

Application profiling is an important technique for efficient resource management. The decision making of scheduling and resource allocation typically takes great advantage of such a technique primarily for improving resource utilization. With the advent of cloud computing as a multitenant virtualized platform, diverse applications are increasingly deployed onto the cloud and they more than often share physical resources. The background load (other applications running on the same physical machine) is therefore an important factor for profiling an application in this cloud computing scenario. In this paper, we present a novel application profiling technique using the canonical correlation analysis (CCA) method, which identifies the relationship between application performance and resource usage. We further devise a performance prediction model based on application profiles generated using CCA. Clearly, our profiling technique with this prediction model has a lot of potentials particularly in virtual machine (VM) placement with performance awareness. Our experimental results demonstrate the capability of our profiling technique and the accuracy of our prediction model.
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paper_title: Dynamic Energy-Efficient Virtual Machine Placement Optimization for Virtualized Clouds

paper_content:

A virtual machine placement strategy based on the trade-off between energy consumption and SLA is presented. Aiming at dynamical changes of workload requirements, a self-adaptive placement strategy RLWR based on robust local weight regression is presented, which could decide the overload time of hosts dynamically. After detecting overloaded hosts, one virtual machine migration selection algorithm MNM is proposed. The MNM’s objective is to get minimal migration number. The migrated virtual machines are deployed using bin-packing algorithm PBFDH. The experimental results show that our algorithm has obvious advantages than other algorithms.
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paper_title: Interference-aware virtual machine placement in cloud computing systems

paper_content:

Cloud computing provides scalable computing and storage resources. These scalable resources can be dynamically organized as many virtual machines (VMs) to run user applications based on a pay-per-use basis. This paper investigates the interference-aware VM placement (IAVMP) problem. In addition to fully exploiting the resources of PMs, the IAVMP problem considers the quality of service (QoS) requirements of user applications and the VM interference reduction. We first formulate the IAVMP problem by an Integer Linear Programming (ILP) model to solve it optimally. Due to the computation complexity of the ILP model, we also propose a polynomial-time heuristic algorithm to efficiently solve the IAVMP problem. Finally, simulation experiments are performed to demonstrate the effectiveness of the proposed heuristic algorithm by comparing with other VM placement algorithms.
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paper_title: Server consolidation with migration control for virtualized data centers

paper_content:

Virtualization has become a key technology for simplifying service management and reducing energy costs in data centers. One of the challenges faced by data centers is to decide when, how, and which virtual machines (VMs) have to be consolidated into a single physical server. Server consolidation involves VM migration, which has a direct impact on service response time. Most of the existing solutions for server consolidation rely on eager migrations, which try to minimize the number of physical servers running VMs. These solutions generate unnecessary migrations due to unpredictable workloads that require VM resizing. This paper proposes an LP formulation and heuristics to control VM migration, which prioritize virtual machines with steady capacity. We performed experiments using TU-Berlin and Google data center workloads to compare our migration control strategy against existing eager-migration-based solutions. We observed that avoiding migration of VMs with steady capacity reduces the number of migrations with minimal penalty in the number of physical servers.
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paper_title: Inter-and-intra data center VM-placement for energy-efficient large-Scale cloud systems

paper_content:

Data centers play the crucial role in the delivery of Cloud services by enabling on-demand access to the shared resources such as software, platform, and infrastructure. Virtual Machine (VM) allocation is one of the challenging tasks in the data center management since user requirements, typically expressed as SLAs, have to be met with the minimum operational expenditure. Despite their huge processing and storage facilities, data centers are among the major contributors of the GreenHouse Gas (GHG) emissions of the IT services. In this paper, we propose a holistic approach for a large-scale Cloud system where the Cloud services are provisioned by several data centers interconnected over the backbone network. We propose a Mixed Integer Linear Programming (MILP) formulation that aims at virtualizing the backbone topology and placing the VMs in data centers with the objective of minimum power consumption. We compare our proposed solution with a benchmark MILP model which selects the closest data centers that can accommodate the user requests taking into account the CPU, memory, and bandwidth capacities of the residing physical hosts. Collected experimental results show the benefit of the proposed management scheme in terms of power consumption, resource utilization, and fairness for medium size data centers.
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paper_title: Provisioning of requests for virtual machine sets with placement constraints in IaaS clouds

paper_content:

We study the problem of optimising the provisioning of collections of virtual machines (VMs) having different placement constraints (e.g., security and anti-collocation) and characteristics (e.g., memory and disk capacity), given a set of physical machines (PMs) with known specifications, in order to achieve the objective of maximising an IaaS cloud provider's revenue. We propose two approaches. The first is based on the formulation of the problem as an integer linear programming (ILP) problem, the solution to which provides an optimal VM placement. The second approach is a heuristic based on classifying the requests into different categories and satisfying the constraints in a particular order using a first lit decreasing (FFD) algorithm for multi-dimensional vector bin packing problem. Given a model of VM placement constraints, offered resources and requests with multiple VM types, both approaches devise a placement plan in a way that maximizes revenue, having due regard both to customer requirements and PM capacities. We evaluate the relative performance of the solutions by means of numerical experiments. The results suggest the optimal solution is not practical for medium to large problems, but it is encouraging that the placement plans of the heuristic are close to those of the optimal solution for smaller problem sizes. We use the heuristic to generate results for large scale placement problems; experiments suggest that it is practical in terms of its runtime efficiency and can provide an effective means of online VM-to-PM mapping.
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paper_title: Efficient VM placement with multiple deterministic and stochastic resources in data centers

paper_content:

Virtual machines (VMs) may significantly improve the efficiency of data center infrastructure by sharing resources of physical servers. This benefit relies on an efficient VM placement scheme to minimize the number of required servers. Existing VM placement algorithms usually assume that VMs' demands for resources are deterministic and stable. However, for certain resources, such as network bandwidth, VMs' demands are bursty and time varying, and demonstrate stochastic nature. In this paper, we study efficient VM placement in data centers with multiple deterministic and stochastic resources. First, we formulate the Multidimensional Stochastic VM Placement (MSVP) problem, with the objective to minimize the number of required servers and at the same time satisfy a predefined resource availability guarantee. Then, we show that the problem is NP-hard, and propose a polynomial time algorithm called Max-Min Multidimensional Stochastic Bin Packing (M3SBP). The basic idea is to maximize the minimum utilization ratio of all the resources of a server, while satisfying the demands of VMs for both deterministic and stochastic resources. Next, we conduct simulations to evaluate the performance of M3SBP. The results demonstrate that M3SBP guarantees the availability requirement for stochastic resources, and M3SBP needs the smallest number of servers to provide the guarantee among the benchmark algorithms.
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paper_title: Data-Aware Virtual Machine Placement and Rate Allocation in Cloud Environment

paper_content:

Infrastructure-as-a-service cloud provides a suitable environment where cloud users can run data-intensive applications and store their required data files. The performance of data-intensive applications strongly depends on data transmission delay. This delay is a function of the size of data files, the location of virtual machines that run the applications as well as the allocation of data rates to virtual machines. In this paper, we propose a novel virtual machine placement algorithm that jointly optimizes the placement of virtual machines as well as their allocated data rates. Through Simulation results we show that the proposed algorithm can significantly reduce the data transfer delay for virtual machines compared to placement algorithms previously proposed in the literature.
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paper_title: Energy efficient VM placement heuristic algorithms comparison for cloud with multidimensional resources

paper_content:

Cloud computing provides user utility-oriented IT services, yet accompanied with huge energy consuming, which contributes to the high operational cost as well as CO2 emission. Making Cloud computing energy efficient can lead to a better tradeoff between profit and environmental impact. In this paper, we formulate the energy efficient VM placement problem in Cloud architecture with multidimensional resources and introduce the objective of this problem. Heuristic algorithms including three traditional local search algorithms and generic algorithm (GA) are presented to provide possible optimized solution. We conduct experiments based on Cloudsim. The result shows that GA sometimes provide the best solution, but with poor stabability. Although the BF provide neither the best nor the worst solution most of time, it have the best stabability.
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paper_title: Energy-Efficient Virtual Machine Replication and Placement in a Cloud Computing System

paper_content:

By utilizing Virtual Machines (VM) and doing server consolidation in a datacenter, a cloud provider can reduce the total energy consumption for servicing his clients with little performance degradation. In particular, the cloud provider can take advantage of dissimilar workloads and by assigning these workloads to the same server, can utilize fewer active servers to service his clients. Placing multiple copies of a VM on different servers and distributing the incoming requests among these VM copies can reduce the resource requirement for each VM copy and help the cloud provider utilize the servers more efficiently. In this paper, the problem of energy-efficient VM placement in a cloud computing system is solved. Precisely, we present an approach that first creates multiple copies of VMs and then uses dynamic programming and local search to place these copies on the physical servers. Simulation results show that the proposed algorithm reduces the total energy consumption by up to 20% with respect to previous work.
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paper_title: Optimizing VM placement for HPC in the cloud

paper_content:

Computing as a service model in cloud has encouraged High Performance Computing to reach out to wider scientific and industrial community. Many small and medium scale HPC users are exploring Infrastructure cloud as a possible platform to run their applications. However, there are gaps between the characteristic traits of an HPC application and existing cloud scheduling algorithms. In this paper, we propose an HPC-aware scheduler and implement it atop Open Stack scheduler. In particular, we introduce topology awareness and consideration for homogeneity while allocating VMs. We demonstrate the benefits of these techniques by evaluating them on a cloud setup on Open Cirrus test-bed.
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paper_title: Dynamic optimization of virtual machine placement by resource usage prediction

paper_content:

In order to save energy with preventing the lack of the resources on servers in data centers, we address the dynamic optimization of virtual machine placement. A decision problem of the virtual machine placement has been formulated as Bin Packing problem. Conventional methods repeat to solve the problem at regular intervals. Live migration is used for changing the virtual machine placement, but some of live migrations are unnecessary; the virtual machine is migrated repeatedly between the physical servers. For the purpose of reducing the unnecessary live migrations, we propose a dynamic optimization of virtual machine placement by resource usage prediction. The proposed method predicts the future resource usage by Auto Regressive Model. The proposed method decides the virtual machine placement by solving the Bin Packing problem with the predicted resource usage.
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paper_title: An Optimization-Based Scheme for Efficient Virtual Machine Placement

paper_content:

According to the important methodology of convex optimization theory, the energy-efficient and scalability problems of modern data centers are studied. Then a novel virtual machine (VM) placement scheme is proposed for solving these problems in large scale. Firstly, by referring the definition of VM placement fairness and utility function, the basic algorithm of VM placement which fulfills server constraints of physical machines is discussed. Then, we abstract the VM placement as an optimization problem which considers the inherent dependencies and traffic between VMs. By given the structural differences of recently proposed data center architectures, we further investigate a comparative analysis on the impact of the network architectures, server constraints and application dependencies on the potential performance gain of optimization-based VM placement. Comparing with the existing schemes, the performance improvements are illustrated from multiple perspectives, such as reducing the number of physical machines deployment, decreasing communication cost between VMs, improving energy-efficient and scalability of data centers.
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paper_title: Server Consolidation Algorithms with Bounded Migration Cost and Performance Guarantees in Cloud Computing

paper_content:

Consolidation of virtual machines is essential to achieve energy optimization in cloud computing environments. As virtual machines dynamically enter and leave a cloud system, it becomes necessary to relocate virtual machines among servers. However, relocation of virtual machines introduces run-time overheads and consumes extra energy, thus an careful planning for relocation is necessary. We model the relocation problem as a modified bin packing problem and propose a new server consolidation algorithm that guarantees server consolidation with {\em bounded} relocation costs. We also conduct a detailed analysis on the complexity of the server consolidation problem, and give a upper bound on the cost of relocation. Finally, we conduct simulations and compare our server consolidation algorithm with other relocation methods, like First Fit and Best Fit method. The experiment results suggest an interesting trade-off between server consolidation quality and relocation cost. Our algorithm is able to trade about 1\% in server consolidation quality for a reduction about 50\% in relocation cost, when compared with other well known bin packing algorithms. We also note that the relocation cost incurred in our method is much less than the theoretical bound we provided. The reason is that we overestimate the amount of relocation from theoretical analysis, and the actual amount of relocation found from experiments is much less than the worst-case bound from theoretical analysis.
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paper_title: Multi-objective Virtual Machine Placement with Service Level Agreement: A Memetic Algorithm Approach

paper_content:

The process of selecting which virtual machines should be located (i.e. executed) at each physical machine of a Datacenter is known as Virtual Machine Placement VMP. This work proposes for the first time a multi-objective formulation of the VMP considering Service Level Agreement. A novel multiobjective memetic algorithm is also proposed to solve the formulated multi-objective problem. This proposal is validated comparing experimental results of the proposed algorithm with a brute force exhaustive search algorithm. Simulations prove the correctness of the proposed memetic algorithm and its scalability considering different experimental scenarios.
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paper_title: Improved energy-efficiency in cloud datacenters with interference-aware virtual machine placement

paper_content:

Virtualization is one of the main technologies used for improving resource efficiency in datacenters; it allows the deployment of co-existing computing environments over the same hardware infrastructure. However, the co-existing of environments — along with management inefficiencies — often creates scenarios of high-competition for resources between running workloads, leading to performance degradation. This phenomenon is known as Performance Interference, and introduces a non-negligible overhead that affects both a datacenter's Quality of Service and its energy-efficiency. This paper introduces a novel approach to workload allocation that improves energy-efficiency in Cloud datacenters by taking into account their workload heterogeneity. We analyze the impact of performance interference on energy-efficiency using workload characteristics identified from a real Cloud environment, and develop a model that implements various decision-making techniques intelligently to select the best workload host according to its internal interference level. Our experimental results show reductions in interference by 27.5% and increased energy-efficiency up to 15% in contrast to current mechanisms for workload allocation.
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paper_title: Guaranteeing High Availability Goals for Virtual Machine Placement

paper_content:

The placement of virtual machines (VMs) on a cluster of hosts under multiple constraints, including administrative (security, regulations) resource-oriented (capacity, energy), and QoS-oriented (performance) is a highly complex task. We define a new high-availability property for a VM, when a VM is marked as k-resilient, as long as there are up to k host failures, it should be guaranteed that it can be relocated to a non-failed host without relocating other VMs. Together with Hardware Predictive Failure Analysis and live migration, which enable VMs to be evacuated from a host before it fails, this property allows the continuous running of VMs on the cluster despite host failures. The complexity of the constraints associated with k-resiliency, which are naturally expressed by Second Order logic statements, prevented their integration into the placement computation until now. We present a novel algorithm which enables this integration by transforming the k-resiliency constraints to rules consumable by a generic Constraint Programming engine, prove that it guarantees the required resiliency and describe the implementation. We provide some preliminary results and compare our high availability support with naive solutions.
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paper_title: Particle Swarm Optimization for Energy-Aware Virtual Machine Placement Optimization in Virtualized Data Centers

paper_content:

A critical research issue is to lower the energy consumption of a virtualized data center by means of virtual machine placement optimization while satisfying the resource requirements of the cloud services. In this paper, we focus on different existing schemes and on the energy-aware virtual machine placement optimization problem of a heterogeneous virtualized data center. We attempt to explore a better alternative approach to minimizing the energy consumption, and we observe that particle swarm optimization (PSO) has considerable potential. However, the PSO must be improved to solve an optimization problem. The improvement includes redefining the parameters and operators of the PSO, adopting an energy-aware local fitness first strategy and designing a novel coding scheme. Using the improved PSO, an optimal virtual machine replacement scheme with the lowest energy consumption can be found. Experimental results indicate that our approach significantly outperforms other approaches, and can lessen 13%-23% energy consumption in the context of this paper.
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paper_title: Intelligent virtual machine placement for cost efficiency in geo-distributed cloud systems

paper_content:

An important challenge of running large-scale cloud services in a geo-distributed cloud system is to minimize the overall operating cost. The operating cost of such a system includes two major components: electricity cost and wide-area-network (WAN) communication cost. While the WAN communication cost is minimized when all virtual machines (VMs) are placed in one datacenter, the high workload at one location requires extra power for cooling facility and results in worse power usage effectiveness (PUE). In this paper, we develop a model to capture the intrinsic trade-off between electricity and WAN communication costs, and formulate the optimal VM placement problem, which is NP-hard due to its binary and quadratic nature. While exhaustive search is not feasible for large-scale scenarios, heuristics which only minimize one of the two cost terms yield less optimized results. We propose a cost-aware two-phase metaheuristic algorithm, Cut-and-Search, that approximates the best trade-off point between the two cost terms. We evaluate Cut-and-Search by simulating it over multiple cloud service patterns. The results show that the operating cost has great potential of improvement via optimal VM placement. Cut-and-Search achieves a highly optimized trade-off point within reasonable computation time, and outperforms random placement by 50%, and the partial-optimizing heuristics by 10-20%.
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paper_title: Energy-aware virtual machine placement in data centers

paper_content:

The energy efficiency of modern data centers has become a practical concern and has attracted significant attention in recent years. In contract to existing solutions that primarily focuses on only one specific aspect of management to reduce energy consumption, this paper explores the balance between server energy consumption and network energy consumption to present an energy-aware joint virtual machine (VM) placement. Given the definition of VM placement fairness, the basic algorithm of VM placement which fulfills server energy consumption constraints is conducted. Then, we further formulate the VM placement as an optimization problem which considers application dependencies to reduce network energy consumption. We design a joint algorithm that efficiently solves the VM placement problem for very large problem sizes. Using simulations, we conduct a comparative analysis on the impact of the data center architectures, server constraints and application dependencies on the potential performance gain of energy-aware VM placement. Compared to existing generic methods, we show a significant performance improvement such as efficiently reducing the number of physical machines used to save server energy consumption, decreasing the communication distance between VMs to obtain data center network energy consumption efficiency, improving scalability of data centers.
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paper_title: A network-aware virtual machine placement algorithm in mobile cloud computing environment

paper_content:

By taking into consideration network features of a wireless mesh network environment where mobile cloud computing (MCC) system is deployed, this paper proposes an effective virtual machine (VM) placement algorithm. This algorithm tends to place a newly created VM for a cloud service to a cloud server, which itself is also a mobile device, that occurs lowest network delay when accessing to application data stored in wireline networks. The aim is to reduce cloud service response time as much as possible in order to deliver better quality of experience to cloud end users over more noisy and less robust wireless networks. In addition, the proposed deployment of cloud storage and cloud computational resources onto wireless networks is also a contribution of the paper. The simulation results have shown the effectiveness and the efficiency of the proposed VM placement algorithms both in terms of cloud service response time.
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paper_title: A Hybrid Genetic Algorithm for the Energy-Efficient Virtual Machine Placement Problem in Data Centers

paper_content:

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation technology. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches consider the energy consumption by physical machines only, but do not consider the energy consumption in communication network, in a data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement. In our preliminary research, we have proposed a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both physical machines and the communication network in a data center. Aiming at improving the performance and efficiency of the genetic algorithm, this paper presents a hybrid genetic algorithm for the energy-efficient virtual machine placement problem. Experimental results show that the hybrid genetic algorithm significantly outperforms the original genetic algorithm, and that the hybrid genetic algorithm is scalable.
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paper_title: Migration-based virtual machine placement in cloud systems

paper_content:

Cloud computing is an emerging technology that greatly shapes our lives, where users run jobs on virtual machines (VMs) on physical machines (PMs) provided by a cloud provider, saving the investment in upfront infrastructures. Due to the heterogeneity of various jobs, different VMs on the same PM can have different job completion times. Meanwhile, the PMs are also heterogeneous. Therefore, different VM placements have different job completion times. Our objective is to minimize the total job completion time of the input VM requests through a reasonable VM placement schedule. This problem is NP-hard, since it can be reduced to a knapsack problem. We propose an off-line VM placement method through emulated VM migration, while the on-line VM placement is solved by a real VM migration process. The migration algorithm is a heuristic approach, where we place the VM to its best PM directly, as long as it has enough capacity. Otherwise, if the migration constraint is satisfied, we migrate another VM from this PM to accommodate the new VM. Furthermore, we study a hybrid scheme where a batch is employed to accept upcoming VMs for the on-line scenario. Evaluation results prove the high efficiency of our algorithms.
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paper_title: An Energy Efficient Virtual Machine Placement Algorithm with Balanced Resource Utilization

paper_content:

Virtualization is a key technology for resource sharing in IaaS/PaaS cloud infrastructures. One primary issue in virtualization is the virtual machine placement (VMP) problem, which is to choose proper physical machine (PM) to deploy virtual machines (VMs) in runtime. In this paper, we study the VMP problem with the goal of minimizing the total energy consumption. We first present a multi-dimensional space partition model to characterize the resource usage states of PMs. Based on this model, we then propose a virtual machine placement algorithm, which can balance the utilization of multi-dimensional resources, reduce the number of running PMs and thus lower down the energy consumption. We also evaluate our proposed balanced algorithm via extensive simulations. Simulation results show that our approach can save as much as 15% energy compared to the first fit algorithm over a long run.
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paper_title: Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement

paper_content:

The scalability of modern data centers has become a practical concern and has attracted significant attention in recent years. In contrast to existing solutions that require changes in the network architecture and the routing protocols, this paper proposes using traffic-aware virtual machine (VM) placement to improve the network scalability. By optimizing the placement of VMs on host machines, traffic patterns among VMs can be better aligned with the communication distance between them, e.g. VMs with large mutual bandwidth usage are assigned to host machines in close proximity. We formulate the VM placement as an optimization problem and prove its hardness. We design a two-tier approximate algorithm that efficiently solves the VM placement problem for very large problem sizes. Given the significant difference in the traffic patterns seen in current data centers and the structural differences of the recently proposed data center architectures, we further conduct a comparative analysis on the impact of the traffic patterns and the network architectures on the potential performance gain of traffic-aware VM placement. We use traffic traces collected from production data centers to evaluate our proposed VM placement algorithm, and we show a significant performance improvement compared to existing general methods that do not take advantage of traffic patterns and data center network characteristics.
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paper_title: Joint VM placement and routing for data center traffic engineering

paper_content:

Today's data centers need efficient traffic management to improve resource utilization in their networks. In this work, we study a joint tenant (e.g., server or virtual machine) placement and routing problem to minimize traffic costs. These two complementary degrees of freedom—placement and routing—are mutually-dependent, however, are often optimized separately in today's data centers. Leveraging and expanding the technique of Markov approximation, we propose an efficient online algorithm in a dynamic environment under changing traffic loads. The algorithm requires a very small number of virtual machine migrations and is easy to implement in practice. Performance evaluation that employs the real data center traffic traces under a spectrum of elephant and mice flows, demonstrates a consistent and significant improvement over the benchmark achieved by common heuristics.
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paper_title: Definition of Security Metrics for the Cloud Computing and Security-Aware Virtual Machine Placement Algorithms

paper_content:

Nowadays, Cloud Computing is becoming a key factor in computer science. Besides the great benefits it brought to the information technology and to the economy, Cloud Computing shows some weakness when looking at the security. An IaaS client should be able to specify its security requirements. But the lack of a system of security metrics leads to the incapability of quantifying the security level of a client deployment in a Cloud. Therefore, we propose a system of security metrics specific to the Cloud Computing and use it to develop virtual machines placement algorithms.
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paper_title: VMPlanner : Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers

paper_content:

In recent years, the power costs of cloud data centers have become a practical concern and have attracted significant attention from both industry and academia. Most of the early works on data center energy efficiency have focused on the biggest power consumers (i.e., computer servers and cooling systems), yet without taking the networking part into consideration. However, recent studies have revealed that the network elements consume 10-20% of the total power in the data center, which poses a great challenge to effectively reducing network power cost without adversely affecting overall network performance. Based on the analysis on topology characteristics and traffic patterns of data centers, this paper presents a novel approach, called VMPlanner, for network power reduction in the virtualization-based data centers. The basic idea of VMPlanner is to optimize both virtual machine placement and traffic flow routing so as to turn off as many unneeded network elements as possible for power saving. We formulate the optimization problem, analyze its hardness, and solve it by designing VMPlanner as a stepwise optimization approach with three approximation algorithms. VMPlanner is implemented and evaluated in a simulated environment with traffic traces collected from a data center test-bed, and the experiment results illustrate the efficacy and efficiency of this approach.
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paper_title: On Theory of VM Placement: Anomalies in Existing Methodologies and Their Mitigation Using a Novel Vector Based Approach

paper_content:

In this paper, we present the methodologies used in existing literature for Virtual Machine (VM) placement, load balancing and server consolidation in a data center environment. While the methodologies may seem fine on the surface, certain drawbacks and anomalies can be uncovered when they are analyzed deeper. We point out those anomalies and draw backs in the existing literature and explain what are the root causes of such anomalies. Then we propose a novel methodology based on vector arithmetic which not only addresses those anomalies but also leads to some interesting theories and algorithms to tackle the above mentioned three functionalities required in managing resources of data centers. We believe that with a strong mathematical base, our methodology has the potential to become the foundation of future models and algorithms in this research area.
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paper_title: Multiobjective Virtual Machine Placement in Cloud Environment

paper_content:

In this paper, the virtual machine placement problem is formulated as a multi-objective optimization problem. The objectives are maximizing profit, maximizing load balancing and minimizing recourse wastage. Results of Genetic algorithms, Non-dominated Sorting Genetic Algorithm and Non-dominated Sorting Genetic Algorithm-II are compared with common solution representation, penalty and benefit values. All the three algorithms reported good solutions whereas GA and NSGA are subjected to premature convergence and duplicate solutions. NSGA-II gives good and diversified range of solutions.
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paper_title: Prevent VM Migration in Virtualized Clusters via Deadline Driven Placement Policy

paper_content:

VM consolidation has been shown as a promising technique for saving energy costs of a data center. It relies on VM migration to move user applications or jobs onto fewer numbers of physical servers during off peak hour. However, VM migration is a costly operation that could cause several concerns, such as performance degradation and system instability. Most existing works were proposed to minimize the migration cost for dynamic consolidation which migrates VM at the runtime when SLA violation or resource under-utilization is detected. In contrast, this paper aims to proactively prevent VM migration for semi-static VM consolidation by proposing a deadline driven VM placement strategy based on the awareness of the server turn-off time and job execution time. We evaluate our approach using a real HPC cluster trace as well as a set of synthetic generated workloads. The results show our approach can significantly reduce the number of migrations by 70% on the real trace. We also demonstrate that our approach can be resilient to different workload patterns by achieving consistent improvement around 50% over all the synthetic workloads.
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paper_title: A Two Stage Heuristic Algorithm for Solving the Server Consolidation Problem with Item-Item and Bin-Item Incompatibility Constraints

paper_content:

The problem of server sprawl is common in data centers of most business organizations. It is most often the case that an application is run on dedicated servers. This leads to situations where organizations end up having numerous servers that remain under-utilized most of the times. The servers, in such scenarios, are allocated more resources (disk, cpu and memory) than are justified by their present workloads. Consolidating multiple underutilized servers into a fewer number of non-dedicated servers that can host multiple applications is an effective tool for businesses to enhance their returns on investment. The problem can be modeled as a variant of the bin packing problem where items to be packed are the servers being consolidated and bins are the target servers. The sizes of the servers/items being packed are resource utilizations which are obtained from the performance trace data. Here we describe a novel two stage heuristic algorithm for taking care of the "bin-item" incompatibility constraints that are inherent in any server consolidation problem. The model is able to solve extremely large instances of problem in a reasonable amount of time.
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paper_title: Virtual machine placement for predictable and time-constrained peak loads

paper_content:

We present an approach to optimal virtual machine placement within datacenters for predicable and time-constrained load peaks. A method for optimal load balancing is developed, based on binary integer programming. For tradeoffs between quality of solution and computation time, we also introduce methods to pre-process the optimization problem before solving it. Upper bound based optimizations are used to reduce the time required to compute a final solution, enabling larger problems to be solved. For further scalability, we also present three approximation algorithms, based on heuristics and/or greedy formulations. The proposed algorithms are evaluated through simulations based on synthetic data sets. The evaluation suggests that our algorithms are feasible, and that these can be combined to achieve desired tradeoffs between quality of solution and execution time.
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paper_title: On Modelling Virtual Machine Consolidation to Pseudo-Boolean Constraints

paper_content:

Cloud Computing is a new paradigm of distributed computing that offers virtualized resources and services over the Internet. To offer Infrastructure-as-a-Service (IaaS) many Cloud providers uses a large data center which usage ranges 5% to 10% of capacity in average. In order to improve Cloud data center management and resources usage a Virtual Machine (VM) consolidation technique can be applied to increase workloads and save energy. Using VM consolidation, we introduce an artificial intelligence consolidation based in Pseudo-Boolean (PB) Constraints to find a optimal consolidation. To evaluate our PB consolidation approach we used the DInf-UFPR and Google Cluster scenario and the formulas are solved with two state-of-the-art solvers.
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paper_title: Time-Aware VM-Placement and Routing with Bandwidth Guarantees in Green Cloud Data Centers

paper_content:

Variation in network performance due to the shared resources is a key obstacle for cloud adoption. Thus, the success of cloud providers to attract more tenants depends on their ability to provide bandwidth guarantees. Power efficiency in data centers has become critically important for supporting larger number of tenants. In this paper, we address the problem of time-aware VM-placement and routing (TVPR), where each tenant requests for a specified amount of server resources (VMs) and network resource (bandwidth) for a given duration. The TVPR problem allocates the required resources for as many tenants as possible by finding the right set of servers to map their VMs and routing their traffic so as to minimize the total power consumption. We propose a multi-component utilization-based power model to determine the total power consumption of a data center according to the resource utilization of the components (servers and switches). We then develop a mixed integer linear programming (MILP) optimization problem formulation based on the proposed power model and prove it to be N P-complete. Since the TVPR problem is computationally prohibitive, we develop a fast and scalable heuristic algorithm. To demonstrate the efficiency of our proposed algorithm, we compare its performance with the numerical results obtained by solving the MILP problem using CPLEX, for a small data center. We then demonstrate the effectiveness of the proposed algorithm in terms of power consumption and acceptance ratio for large data centers through simulation results.
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paper_title: Online traffic-aware virtual machine placement in data center networks

paper_content:

Data centers rely on virtualization to provide different services over a shared infrastructure. The placement of the different services and tasks in the physical machines is crucial for the performance of the whole system. A misplaced service can overload some network links, lead to congestion, or even connection disruptions. On the other hand, virtual machine migration allows reallocating services and changing the traffic matrix, leading to more efficient use of bandwidth. In this paper, we propose a Virtual Machine Placement (VMP) algorithm to (re)allocate virtual machines in the data center servers, based on the current traffic matrix, CPU, and memory usage. Analyzing the formation of community patterns in terms of traffic using graph theory, we are able to find virtual machines that are correlated because they exchange high amount of data. Those virtual machines are aggregated and allocated to servers as close as possible to each other, reducing traffic congestion. Our simulation results show that VMP was able to improve the traffic distribution. In some specific cases we were able to reduce 80% of the core traffic, concentrating it at the edge of the network.
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paper_title: A simulated annealing algorithm for energy efficient virtual machine placement

paper_content:

Improving energy efficiency has become increasingly important in data centers in recent years to reduce the rapidly growing tremendous amounts of electricity consumption. The power dissipation of the physical servers is the root cause of power usage of other systems, such as cooling systems. Many efforts have been made to make data centers more energy efficient. One of them is to minimize the total power consumption of these servers in a data center through virtual machine consolidation, which is implemented by virtual machine placement. The placement problem is often modeled as a bin packing problem. Due to the NP-hard nature of the problem, heuristic solutions such as First Fit and Best Fit algorithms have been often used and have generally good results. However, their performance leaves room for further improvement. In this paper we propose a Simulated Annealing based algorithm, which aims at further improvement from any feasible placement. This is the first published attempt of using SA to solve the VM placement problem to optimize the power consumption. Experimental results show that this SA algorithm can generate better results, saving up to 25 percentage more energy than First Fit Decreasing in an acceptable time frame.
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paper_title: Virtual Machine Placement Optimization Supporting Performance SLAs

paper_content:

Cloud computing model separates usage from ownership in terms of control on resource provisioning. Resources in the cloud are projected as a service and are realized using various service models like IaaS, PaaS and SaaS. In IaaS model, end users get to use a VM whose capacity they can specify but not the placement on a specific host or with which other VMs it can be co-hosted. Typically, the placement decisions happen based on the goals like minimizing the number of physical hosts to support a given set of VMs by satisfying each VMs capacity requirement. However, the role of the VMM usage to support I/O specific workloads inside a VM can make this capacity requirement incomplete. I/O workloads inside VMs require substantial VMM CPU cycles to support their performance. As a result, placement algorithms need to include the VMM's usage on a per VM basis. Secondly, cloud centers encounter situations wherein change in existing VM's capacity or launching of new VMs need to be considered during different placement intervals. Usually, this change is handled by migrating existing VMs to meet the goal of optimal placement. We argue that VM migration is not a trivial task and does include loss of performance during migration. We quantify this migration overhead based on the VM's workload type and include the same in placement problem. One of the goals of the placement algorithm is to reduce the VM's migration prospects, thereby reducing chances of performance loss during migration. This paper evaluates the existing ILP and First Fit Decreasing (FFD) algorithms to consider these constraints to arrive at placement decisions. We observe that ILP algorithm yields optimal results but needs long computing time even with parallel version. However, FFD heuristics are much faster and scalable algorithms that generate a sub-optimal solution, as compared to ILP, but in time-scales that are useful in real-time decision making. We also observe that including VM migration overheads in the placement algorithm results in a marginal increase in the number of physical hosts but a significant, of about 84 percent reduction in VM migration.
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paper_title: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing

paper_content:

Virtual machine placement is a process of mapping virtual machines to physical machines. The optimal placement is important for improving power efficiency and resource utilization in a cloud computing environment. In this paper, we propose a multi-objective ant colony system algorithm for the virtual machine placement problem. The goal is to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption. The proposed algorithm is tested with some instances from the literature. Its solution performance is compared to that of an existing multi-objective genetic algorithm and two single-objective algorithms, a well-known bin-packing algorithm and a max-min ant system (MMAS) algorithm. The results show that the proposed algorithm is more efficient and effective than the methods we compared it to.
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paper_title: Design and Implementation of a Multi-objective Optimization Mechanism for Virtual Machine Placement in Cloud Computing Data Center

paper_content:

Cloud computing is becoming a popular way of supplying and using computing resources. A cloud-computing data center is equipped with a large number of physical resources and must manage an even larger number of virtual machines (VMs). The center’s VM placement strategy affects the utilization of physical resources, and consequently, it influences operational costs. Our goal is to develop a multi-objective optimization mechanism for VM placement that satisfies various constraints and results in the lowest operational cost. The number of possible combinations of VMs and hosts can be extremely large. For the mechanism to be practical, the number of possible combinations must be reduced. We reduced computational overheads by classifying VM hosts into a relatively small number of equivalent sets. Simulation results show that expected operational costs can be significantly reduced by applying the proposed mechanism.
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paper_title: An Availability-Aware Virtual Machine Placement Approach for Dynamic Scaling of Cloud Applications

paper_content:

Cloud computing promises customers the on-demand ability to dynamically provision virtualization resources in face of workload variations. Most existing scaling approaches addressed this problem by allocating application to a certain amount of cloud resources. However, the problem of the availability of application influenced by VM-based physical locations during resource scaling process is a serious challenge due to dynamic complex workload and has not been widely discussed yet. In this paper, we present a novel availability-based computing model to describe availability attribute of one application in the hierarchical structured cloud. Moreover, we propose an availability-aware policy by performing both vertical and horizontal scaling to explore how and where to allocate computing resource. Simulation results indicate that our model captured the availability of cloud applications properly and proposed scaling approach achieves the objectives of meeting availability demands and minimizing the total communication cost.
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paper_title: Improving Performance and Availability of Services Hosted on IaaS Clouds with Structural Constraint-Aware Virtual Machine Placement

paper_content:

The increasing popularity of modern virtualization-based datacenters continues to motivate both industry and academia to provide answers to a large variety of new and challenging questions. In this paper we aim to answer focusing on one such question: how to improve performance and availability of services hosted on IaaS clouds. Our system, structural constraint-aware virtual machine placement (SCAVP), supports three types of constraints: demand, communication and availability. We formulate SCAVP as an optimization problem and show its hardness. We design a hierarchical placement approach with four approximation algorithms that efficiently solves the SCAVP problem for large problem sizes. We provide a formal model for the application (to better understand structural constraints) and the datacenter (to effectively capture capabilities), and use the two models as inputs to the placement problem. We evaluate SCAVP in a simulated environment to illustrate the efficiency and importance of the proposed approach.
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paper_title: An energy-aware heuristic framework for virtual machine consolidation in Cloud computing

paper_content:

Virtual machine (VM) consolidation in Cloud computing provides a great opportunity for energy saving. However, the obligation of providing suitable quality of service to end users leads to the necessity in dealing with energy-performance tradeoff. In this paper, we propose a redesigned energy-aware heuristic framework for VM consolidation to achieve a better energy-performance tradeoff. There are two main contributions in the framework: (1) establish a service level agreement (SLA) violation decision algorithm to decide whether a host is overload with SLA violation; (2) minimum power and maximum utilization policy is then proposed to improve the Minimum Power policy in previous work. Finally, we have evaluated our framework through simulation on large-scale experiments driven by workload traces from more than a thousand VMs, and the results show that our framework outperforms previous work. Specifically, it guarantees 21---34 % decrease in energy consumption, 84---92 % decrease in SLA violation, 87---94 % decrease in energy-performance metric, and 63 % decrease in execution time. And we further discuss why the redesigned framework outperforms the previous design.
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paper_title: VM Placement Strategies for Cloud Scenarios

paper_content:

The problem of Virtual Machine (VM) placement in a compute cloud infrastructure is well-studied in the literature. However, the majority of the existing works ignore the dynamic nature of the incoming stream of VM deployment requests that continuously arrive to the cloud provider infrastructure. In this paper we provide a practical model of cloud placement management under a stream of requests and present a novel technique called Backward Speculative Placement (BSP)that projects the past demand behavior of a VM to a candidate target host. We exploit the BSP technique in two algorithms, first for handling the stream of deployment requests, second in a periodic optimization, to handle the dynamic aspects of the demands. We show the benefits of our BSPtechnique by comparing the results on a simulation period with a strategy of choosing an optimal placement at each time instant, produced by a generic MIP solver.
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paper_title: Redundant virtual machine placement for fault-tolerant consolidated server clusters

paper_content:

Consolidated server systems using server virtualization involves serious risks of host server failures that induce unexpected downs of all hosted virtual machines and applications. To protect applications requiring high-availability from unpredictable host server failures, redundant configuration using virtual machines can be an effective countermeasure. This paper presents a virtual machine placement method for establishing a redundant configuration against host server failures with less host servers. The proposed method estimates the requisite minimum number of virtual machines according to the performance requirements of application services and decides an optimum virtual machine placement so that minimum configurations survive at any k host server failures. The evaluation results clarify that the proposed method achieves requested fault-tolerance level with less number of hosting servers compared to the conventional N+M redundant configuration approach.
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paper_title: Elasticity-aware virtual machine placement for cloud datacenters

paper_content:

With the increasing popularity of cloud computing, the cloud datacenter suffers from both limited resources and the variation of users' requests. One important feature of cloud computing is on-demand scaling, enabling the fluctuation of one user's resource demand. However, amongst previous work concerning the virtual machine (VM) placement in datacenters, satisfying the VMs' requested resources of users is the primary objective, neglecting future demand variation. In this paper, we propose the concept of elasticity, referring to how well the datacenter can satisfy the growth of the input VMs resource demands under both the limitations of physical machines (PMs) capacities and links capacities. To consider both dimensions of the machine and bandwidth resources simultaneously, we propose our hierarchical VM placement algorithm. We also prove the optimality of our algorithm in a frequently used semi-homogeneous datacenter configuration. Furthermore, we study the heterogeneous datacenter configuration, favoring the characteristics of multi-tenant datacenters. Evaluation results validate the efficiency of our algorithm.
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paper_title: A Matrix Transformation Algorithm for Virtual Machine Placement in Cloud

paper_content:

The virtual machine (VM) placement is an important problem of cloud data center resource management. There have been many research efforts to address this problem. However, existing research is mainly concerned with single objective such as the number of the physical machine (PM) used, and few research consider the resource utilization rate of PM's different dimensions. In this paper, we present a two-objective optimization model such that the number of required PM is minimized and the resource utilization of occupied PM is optimum. We further provide an algorithm based on matrix transformation. In the algorithm, we define cluster matrix of VM request sequence and VM placement initial matrix to generate the optimum solution of VM placement optimization model. Finally, we compare our algorithm with the other VM placement strategy, the experimental results demonstrate the good performance of our algorithm.
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paper_title: Virtual Machine Placement for Improving Energy Efficiency and Network Performance in IaaS Cloud

paper_content:

Under the premise of ensuring application performance, how to place virtual machines (VMs) on physical machines (PMs) to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers in IaaS cloud. The existing VM placement schemes are mostly to reduce energy consumption by optimizing utilization of physical server or network elements, but the issue of aggressive consolidation of VM is ignored, which may lead to network performance degradation. To address the issue, this paper proposes a VM placement scheme based on a new two-stage heuristic algorithm to optimize network performance and to reduce energy consumption of physical servers and network elements, so as to finally achieve the tradeoff between energy efficiency and network performance. The simulations show that our solution achieves good results.
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paper_title: Energy-Saving Virtual Machine Placement in Cloud Data Centers

paper_content:

In cloud data centers, different mapping relationships between virtual machines (VMs) and physical machines (PMs) cause different resource utilization, therefore, how to place VMs on PMs to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers. The existing VM placement schemes are to optimize physical server resources utilization or network resources utilization, but few of them focuses on optimizing multiple resources utilization simultaneously. To address the issue, this paper proposes a VM placement scheme meeting multiple resource constraints, such as the physical server size (CPU, memory, storage, bandwidth, etc.) and network link capacity to improve resource utilization and reduce both the number of active physical servers and network elements so as to finally reduce energy consumption. Since VM placement problem is abstracted as a combination of bin packing problem and quadratic assignment problem, which is also known as a classic combinatorial optimization and NP-hard problem, we design a novel greedy algorithm by combining minimum cut with the best-fit, and the simulations show that our solution achieves better results.
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paper_title: Energy-Efficient Virtual Machine Placement in Data Centers by Genetic Algorithm

paper_content:

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches to the virtual machine placement problem consider the energy consumption by physical machines in a data center only, but do not consider the energy consumption in communication network in the data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement in order to make the data center more energy-efficient. In this paper, we propose a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both the servers and the communication network in the data center. Experimental results show that the genetic algorithm performs well when tackling test problems of different kinds, and scales up well when the problem size increases.
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paper_title: Shadow-routing based dynamic algorithms for virtual machine placement in a network cloud

paper_content:

We consider a shadow routing based approach to the problem of real-time adaptive placement of virtual machines (VM) in large data centers (DC) within a network cloud. Such placement in particular has to respect vector packing constraints on the allocation of VMs to host physical machines (PM) within a DC, because each PM can potentially serve multiple VMs simultaneously. Shadow routing is attractive in that it allows a large variety of system objectives and/or constraints to be treated within a common framework (as long as the underlying optimization problem is convex). Perhaps even more attractive feature is that the corresponding algorithm is very simple to implement, it runs continuously, and adapts automatically to changes in the VM demand rates, changes in system parameters, etc., without the need to re-solve the underlying optimization problem “from scratch”. In this paper we focus on the minmax-DC-load problem. Namely, we propose a combined VM-toDC routing and VM-to-PM assignment algorithm, referred to as Shadow scheme, which minimizes the maximum of appropriately defined DC utilizations. We prove that the Shadow scheme is asymptotically optimal (as one of its parameters goes to 0). Simulation confirms good performance and high adaptivity of the algorithm. Favorable performance is also demonstrated in comparison with a baseline algorithm based on VMware implementation [7], [8]. We also propose a simplified - “more distributed” - version of the Shadow scheme, which performs almost as well in simulations.
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paper_title: Optimizing data access latencies in cloud systems by intelligent virtual machine placement

paper_content:

Many cloud applications are data intensive requiring the processing of large data sets and the MapReduce/Hadoop architecture has become the de facto processing framework for these applications. Large data sets are stored in data nodes in the cloud which are typically SAN or NAS devices. Cloud applications process these data sets using a large number of application virtual machines (VMs), with the total completion time being an important performance metric. There are many factors that affect the total completion time of the processing task such as the load on the individual servers, the task scheduling mechanism, communication and data access bottlenecks, etc. One dominating factor that affects completion times for data intensive applications is the access latencies from processing nodes to data nodes. Ideally, one would like to keep all data access local to minimize access latency but this is often not possible due to the size of the data sets, capacity constraints in processing nodes which constrain VMs from being placed in their ideal location and so on. When it is not possible to keep all data access local, one would like to optimize the placement of VMs so that the impact of data access latencies on completion times is minimized. We address this problem of optimized VM placement - given the location of the data sets, we need to determine the locations for placing the VMs so as to minimize data access latencies while satisfying system constraints. We present optimal algorithms for determining the VM locations satisfying various constraints and with objectives that capture natural tradeoffs between minimizing latencies and incurring bandwidth costs. We also consider the problem of incorporating inter-VM latency constraints. In this case, the associated location problem is NP-hard with no effective approximation within a factor of 2 - ϵ for any ϵ > 0. We discuss an effective heuristic for this case and evaluate by simulation the impact of the various tradeoffs in the optimization objectives.
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paper_title: Reduce Energy Consumption through Virtual Machine Placement in Cloud Data Centre

paper_content:

In this paper, energy consumption in the data centre was studied where thousands of servers and other devices runs, energy are utilized to run the server and cooling the environment. Energy consumption can be reduced by switching off the idle server by means of migration of Virtual Machine from under-load Host. Load in cloud computing is maintained by migration of VM from the overloaded Host to a free Host or activate new Host. Based on this study, a reservation technique by using BIN packing was proposed in this paper with an overload detection algorithm. The proposed algorithm RBIN is experimented in 800 servers with 1024 Virtual Machines. From the experimental result, proposed method RBIN reduces energy in higher level.
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paper_title: CCAP: A Cache Contention-Aware Virtual Machine Placement Approach for HPC Cloud

paper_content:

Applications in High Performance Computing (HPC) cloud are characterized by large cache resource consumption due to large-scale inputs and intensive communications, which creates serious Shared Last Level cache (SLLC) performance bottleneck. Current system software stacks are not efficient in addressing this issue among virtual machines at the hypervisor level or the threads at the operating system level. In this paper, we investigate performance interference due to contention for SLLC in the HPC cloud. We employ an enhanced reuse distance analysis technique with an accelerated cyclic compression algorithm to identify application's cache interference intensity. Based on reuse distance analysis, we propose a practical Cache Contention-Aware virtual machine Placement approach (CCAP). CCAP dispatches virtual machines according to their cache interference intensities to avoid cache pollution and interference, thus alleviating negative effects of cache contention. We implement CCAP in the Xen hypervisor. Evaluation of NPB workload reveals that CCAP can improve performance of cache sensitive applications when they are co-scheduled with cache pollution programs. For a 2-workload system, it reduces execution time by 12 %, as well as cache miss rate by 13 %, while increasing throughput by 13 %, on average. Moreover, CCAP also improves the average performance of the cache pollution programs by 5 %. For a 4-workload system, CCAP brings more significant performance improvement to cache sensitive applications, an average increase of 20 %.
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paper_title: Workload characteristics-aware virtual machine consolidation algorithms

paper_content:

An energy conservation strategy must address two issues — placement of virtual machine images and workload characteristics of virtual machines. For performance reason most cloud systems copy a prototype image into the local disk of a physical machine before starting a virtual machine. If the physical machine that stores the image of a virtual machine is off-line, then we cannot run this virtual machine. The workload characteristics of virtual machines determine whether it is data-intensive or CPU-intensive. We assume that the system has a distributed file system therefore a physical machine can run any virtual machine even if it does not have the image. However, we observe that the performance of a data-intensive virtual machine running on a physical machine without its image could result in 60% performance loss compared with running the same virtual machine on a physical machine that has the virtual machine image. On the other hand, the performance of a CPU-intensive virtual machine is almost independent of whether the physical machine has the image or not. As a result, an energy conservation algorithm must consider the workload characteristic of a virtual machine when finding a physical machine to run it, especially for data-intensive virtual machines. This paper proposes a workload characteristics-aware virtual machine consolidation algorithms. We propose an approximation algorithm and two dynamic programmings to consolidate virtual machines and reduce the number of physical machines. We conduct experiments and compare the numbers of physical machines used by our approximation algorithm with the optimal number of physical machines found by our dynamic programming. The experiment results indicate that our approximation algorithm finds good solutions much faster than the dynamic programming.
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paper_title: An energy aware framework for virtual machine placement in cloud federated data centres

paper_content:

Data centres are powerful ICT facilities which constantly evolve in size, complexity, and power consumption. At the same time users' and operators' requirements become more and more complex. However, existing data centre frameworks do not typically take energy consumption into account as a key parameter of the data centre's configuration. To lower the power consumption while fulfilling performance requirements we propose a flexible and energy-aware framework for the (re)allocation of virtual machines in a data centre. The framework, being independent from the data centre management system, computes and enacts the best possible placement of virtual machines based on constraints expressed through service level agreements. The framework's flexibility is achieved by decoupling the expressed constraints from the algorithms using the Constraint Programming (CP) paradigm and programming language, basing ourselves on a cluster management library called Entropy. Finally, the experimental and simulation results demonstrate the effectiveness of this approach in achieving the pursued energy optimization goals.
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paper_title: A Relationship-Based VM Placement Framework of Cloud Environment

paper_content:

Managing computation resources in a cost-effective way has become the core competence for a Cloud provider to win over the market because of the "pay-as-you-go" business model. Therefore, VM placement has become more and more important in the research and practices of VM management by determining at what condition and on which physical server a VM should be placed so that the SLA can be guaranteed and servers' utilization can be improved. Much existing work simply formulates the above issue to be a bin-packing problem, which does not take the VM relationships into account. However, the relationship information can greatly impact the SLA of the Cloud system and the resource utilization. Therefore, in this paper, we propose a relationship-based VM placement framework, SmartCRS, to optimize the VM placement procedure. SmartCRS reveals the relationships between VMs automatically. Then by using such information and based on a constraint library, it gives a proper VM placement plan. Finally, the plan is carried out by SmartCRS automatically or by Cloud administrators manually to improve the server utilization and guarantee the required SLA. Two case studies are conducted to demonstrate the effectiveness and efficiency of the proposed framework at the end of this paper.
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paper_title: EQVMP: Energy-efficient and QoS-aware virtual machine placement for software defined datacenter networks

paper_content:

To provide effective and reliable services, cloud datacenters need parallel computing and virtualization techniques. This work presents an improved virtual machine (VM) placement mechanism, called Energy efficiency and Quality of Service (QoS) aware VM Placement (EQVMP) to overcome the problem of unbalanced traffic load in switching on and off VMs for the purpose of energy saving. EQVMP combines three key techniques: (1) hop reduction, (2) energy saving and (3) load balancing. Hop reduction can regroup VMs to lower the traffic load among them. Energy saving techniques aim at choosing the appropriate servers. The proposed load balancing updates VM placement periodically. Our experimental results show that the proposed scheme can lower energy consumption and maintain QoS. We propose an evaluation score [1] to assess VM placement in terms of energy, delay and throughput. Comparing to other existing placement policies, our proposed mechanism can enhance system throughput by 25% and can have better evaluation score.
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paper_title: Optimal update frequency model for physical machine state change and virtual machine placement in the cloud

paper_content:

Cloud computing is evolving into the default operational framework running modern data centers. Efficient data center operation is concerned with the total amount of energy consumed as well as assuring adequate resources are available to process all of the incoming work requests. Existing research has demonstrated several algorithms that can be used to determine the optimal number of resources required to service these requests. However, a key issue not addressed in these algorithms is determining the frequency of recalculating the number of required resources. Changing the required resources at a rate slower than the optimal update frequency results in lower energy efficiency due to the over allocation of resources. Changing the resources at a rate higher than the optimal frequency results in insufficient time for systems to change state, which results in SLA violations. In this paper, a stochastic optimization model is presented that determines the optimal update frequency for changing the states of the nodes of the cloud as well as determining the proper frequency for recalculating the maximum expected load, which improves the determination of the optimum number of resources required, therefore maximizes energy efficiency and minimizes SLA violations.
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paper_title: Network-aware virtual machine consolidation for large data centers

paper_content:

Resource management in modern data centers has become a challenging task due to the tremendous growth of data centers. In large virtual data centers, performance of applications is highly dependent on the communication bandwidth available among virtual machines. Traditional algorithms either do not consider network I/O details of the applications or are computationally intensive. We address the problem of identifying the virtual machine clusters based on the network traffic and placing them intelligently in order to improve the application performance and optimize the network usage in large data center. We propose a greedy consolidation algorithm that ensures the number of migrations is small and the placement decisions are fast, which makes it practical for large data centers. We evaluated our approach on real world traces from private and academic data centers, using simulation and compared the existing algorithms on various parameters like scheduling time, performance improvement and number of migrations. We observed a ~70% savings of the interconnect bandwidth and overall ~60% improvements in the applications performances. Also, these improvements were produced within a fraction of scheduling time and number of migrations.
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paper_title: Energy Efficient VM Placement Supported by Data Analytic Service

paper_content:

The popularity and commercial use of cloud computing has prompted an increased concern among cloud service providers for energy efficiency while still maintaining quality of service. One of the key techniques used for the efficient use of cloud server resources is virtual machine placement. This work introduces a precise VM placement algorithm that ensures energy efficiency and also prevents Service Level Agreement (SLA) violation. The mathematical model of the algorithm is supported by a sophisticated data analytic system implemented as a service. The precision of the algorithm is achieved by allowing each individual VM to build its own data model on demand over an appropriate time horizon. Thus the data model can reflect accurately the characteristics of resource usage of the VM. The algorithm can communicate synchronously or asynchronously with the data analytic service which is deployed as a cloud-based solution. In the experiments, several advanced data modelling and use forecasting techniques were evaluated. Results from simulation-based experiments show that the VM placement algorithm (supported by the data analytic service) can effectively reduce power consumption, the number of VM migrations, and prevent SLA violation, it also compares very favourably with other placement algorithms.
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paper_title: Maximum migration time guarantees in dynamic server consolidation for virtualized data centers

paper_content:

Server consolidation is a vital mechanism in modern data centers in order to minimize expenses with infrastructure. In most cases, server consolidation may require migrating virtual machines between different physical servers. Although the downtime of live-migration is negligible, the amount of time to migrate all virtual machines can be substantial, delaying the completion of the consolidation process. This paper proposes a new server consolidation algorithm, which guarantees that migrations are completed in a given maximum time. The migration time is estimated using the max-min fairness model, in order to consider the competition of migration flows for the network infrastructure. The algorithm was simulated using a real workload and shows a good consolidation ratio in comparison to other algorithms, while also guaranteeing a maximum migration time.
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paper_title: A Stable Network-Aware VM Placement for Cloud Systems

paper_content:

Virtual Machine (VM) placement has to carefully consider the aggregated resource consumption of co-located VMs in order to obey service level agreements at lower possible cost. In this paper, we focus on satisfying the traffic demands of the VMs in addition to CPU and memory requirements. This is a much more complex problem both due to its quadratic nature (being the communication between a pair of VMs) and since it involves many factors beyond the physical host, like the network topologies and the routing scheme. Moreover, traffic patterns may vary over time and predicting the resulting effect on the actual available bandwidth between hosts within the data center is extremely difficult. We address this problem by trying to allocate a placement that not only satisfies the predicted communication demand but is also resilient to demand time-variations. This gives rise to a new optimization problem that we call the Min Cut Ratio-aware VM Placement (MCRVMP). The general MCRVMP problem is NP-Hard, hence, we introduce several heuristics to solve it in reasonable time. We present extensive experimental results, associated with both placement computation and run-time performance under time-varying traffic demands, to show that our heuristics provide good results (compared to the optimal solution) for medium size data centers.
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paper_title: PBFVMC: A New Pseudo-Boolean Formulation to Virtual-Machine Consolidation

paper_content:

Over the course of the last decade, there have been several improvements in the performance of Boolean Satisfiability (SAT), Integer Linear Programming (ILP) and Pseudo-Boolean Optimization (PBO) solvers. These improvements have encouraged the applications of SAT, ILP and PBO techniques in modeling complex engineering problems. One such problem is the Virtual Machine Consolidation. The Virtual Machine Consolidation problem consists in placing a set of virtual machines in a set of hardware in a way to increase workload on hardware where they can operate more energy-efficient. This paper proposes an improved PBO formulation of the Virtual Machine Consolidation problem, PBFVMC. The improved formulation and enhancements are built on top of a previous work and a new set of constraints is created and rationalized to work more friendly with current PBO solvers. It is observed that this new formulation goes a step ahead and more problems can now be solved.
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paper_title: QoS-Aware VM Placement in Multi-domain Service Level Agreements Scenarios

paper_content:

Virtualization technologies of Infrastructure-as-a- Service enable the live migration of running Virtual Machines (VMs) to achieve load balancing, fault-tolerance and hardware consolidation in data centers. However, the downtime/service unavailability due to live migration may be substantial with relevance to the customers' expectations on responsiveness, as the latter are declared in established Service Level Agreements (SLAs). Moreover, it may cause significant (potentially exponential) SLA violation penalties to its associated higher- level domains (Platform-as-a-Service and Software-as-a-Service). Therefore, VM live migration should be managed carefully. In this paper, we present the OpenStack version of the Generic SLA Manager, alongside its strategies for VM selection and allocation during live migration of VMs. We simulate a use case where IaaS (OpenStack-SLAM) and PaaS (OpenShift) are combined, and assess performance and efficiency of the aforementioned VM placement strategies, when a multi-domain SLA pricing & penalty model is involved. We find that our proposal is efficient in managing trade-offs between the operational objectives of service providers (including financial considerations) and the customers' expected QoS requirements.
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paper_title: Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center

paper_content:

Powerful data centers are the essential supporting infrastructure for mobile, ubiquitous, and cognitive computing, which are the most popular computing paradigms to utilize all kinds of physical resources and provide various services. To ensure the high quality of services, the performance and cost of a data center is a critical factor. In this paper, we investigate the issue of increasing the resource utilization of data centers to improve their performance and lower the cost. It is an efficient way to increase resource utilization via resource sharing. Technically, server virtualization provides the opportunity to share resources in data centers. However, it also introduces other problems, the primary problem being virtual machine placement (VMP), which is to choose a proper physical machine (PM) to deploy virtual machines (VMs) in runtime. We study the virtual machine placement problem with the target of minimizing the total energy consumption by the running of PMs, which is also an indication of resource utilization and the cost of a data center. Due to the multiple dimensionality of physical resources, there always exists a waste of resources, which results from the imbalanced use of multi-dimensional resources. To characterize the multi-dimensional resource usage states of PMs, we present a multi-dimensional space partition model. Based on this model, we then propose a virtual machine placement algorithm EAGLE, which can balance the utilization of multi-dimensional resources, reduce the number of running PMs, and thus lower the energy consumption. We also evaluate our proposed balanced algorithm EAGLE via extensive simulations and experiments on real traces. Experimental results show, over the long run, that EAGLE can save as much as 15% more energy than the first fit algorithm.
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paper_title: A Network-aware Virtual Machine Placement and Migration Approach in Cloud Computing

paper_content:

Cloud computing represents a major step up in computing whereby shared computation resources are provided on demand. In such a scenario, applications and data thereof can be hosted by various networked virtual machines (VMs). As applications, especially data-intensive applications, often need to communicate with data frequently, the network I/O performance would affect the overall application performance significantly. Therefore, placement of virtual machines which host an application and migration of these virtual machines while the unexpected network latency or congestion occurs is critical to achieve and maintain the application performance. To address these issues, this paper proposes a virtual machine placement and migration approach to minimizing the data transfer time consumption. Our simulation studies suggest that the proposed approach is effective in optimizing the data transfer between the virtual machine and data, thus helping optimize the overall application performance.
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paper_title: Power-Efficient Virtual Machine Placement and Migration in Data Centers

paper_content:

In this paper, we propose a power-efficient solution for virtual machine placement and migration in a fat tree data center network. This solution reduces power consumption as well as job delay by aggregating virtual machines to a few hyper visors and migrating communicating parties to close locations. In this work, we consider OpenFlow as the implementation protocol. In an OpenFlow environment, a centralized controller oversees job loads, virtual machine requirements and hardware availability. Given observation of such global knowledge, the OpenFlow controller can schedule jobs and distribute virtual machines accordingly. As jobs change and flows shift, the OpenFlow controller dynamically adjusts virtual machine assignments by aggregating virtual machines to close locations in order to save energy. With this placement and migration proposal, more jobs can operate concurrently with close sources and destinations of flows, thus both job and flow delay can be reduced.
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paper_title: Hierarchical Virtual Machine Consolidation in a Cloud Computing System

paper_content:

Improving the energy efficiency of cloud computing systems has become an important issue because the electric energy bill for 24/7 operation of these systems can be quite large. The focus of this paper is on the virtual machine (VM) consolidation in a cloud computing system as a way of lowering daily energy consumption of the system. In contrast to the existing works that assume resource demands of VMs are known and given as scalar variables, this paper treats these demands as random variables with known means and standard deviations. These random variables may be correlated with one another, and there are several kinds of resources which can be performance bottlenecks. Therefore, both the correlation and multiple resource type should be considered. The VM consolidation problem is then formulated as a multi-capacity stochastic bin packing problem. This problem is NP-hard, so we propose a heuristic method to solve the problem efficiently. The simulation results show that, in spite of its simplicity and scalability, the proposed method produces high quality solutions.
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paper_title: Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments

paper_content:

Server consolidation using virtualization technology has become increasingly important for improving data center efficiency. It enables one physical server to host multiple independent virtual machines (VMs), and the transparent movement of workloads from one server to another. Fine-grained virtual machine resource allocation and reallocation are possible in order to meet the performance targets of applications running on virtual machines. On the other hand, these capabilities create demands on system management, especially for large-scale data centers. In this paper, a two-level control system is proposed to manage the mappings of workloads to VMs and VMs to physical resources. The focus is on the VM placement problem which is posed as a multi-objective optimization problem of simultaneously minimizing total resource wastage, power consumption and thermal dissipation costs. An improved genetic algorithm with fuzzy multi-objective evaluation is proposed for efficiently searching the large solution space and conveniently combining possibly conflicting objectives. The simulation-based evaluation using power-consumption and thermal-dissipation models based on profiling of a Blade Center, demonstrates the good performance, scalability and robustness of our proposed approach. Compared with four well-known bin-packing algorithms and two single-objective approaches, the solutions obtained from our approach seek good balance among the conflicting objectives while others cannot.
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paper_title: Profiling Applications for Virtual Machine Placement in Clouds

paper_content:

Application profiling is an important technique for efficient resource management. The decision making of scheduling and resource allocation typically takes great advantage of such a technique primarily for improving resource utilization. With the advent of cloud computing as a multitenant virtualized platform, diverse applications are increasingly deployed onto the cloud and they more than often share physical resources. The background load (other applications running on the same physical machine) is therefore an important factor for profiling an application in this cloud computing scenario. In this paper, we present a novel application profiling technique using the canonical correlation analysis (CCA) method, which identifies the relationship between application performance and resource usage. We further devise a performance prediction model based on application profiles generated using CCA. Clearly, our profiling technique with this prediction model has a lot of potentials particularly in virtual machine (VM) placement with performance awareness. Our experimental results demonstrate the capability of our profiling technique and the accuracy of our prediction model.
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paper_title: Dynamic Energy-Efficient Virtual Machine Placement Optimization for Virtualized Clouds

paper_content:

A virtual machine placement strategy based on the trade-off between energy consumption and SLA is presented. Aiming at dynamical changes of workload requirements, a self-adaptive placement strategy RLWR based on robust local weight regression is presented, which could decide the overload time of hosts dynamically. After detecting overloaded hosts, one virtual machine migration selection algorithm MNM is proposed. The MNM’s objective is to get minimal migration number. The migrated virtual machines are deployed using bin-packing algorithm PBFDH. The experimental results show that our algorithm has obvious advantages than other algorithms.
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paper_title: Interference-aware virtual machine placement in cloud computing systems

paper_content:

Cloud computing provides scalable computing and storage resources. These scalable resources can be dynamically organized as many virtual machines (VMs) to run user applications based on a pay-per-use basis. This paper investigates the interference-aware VM placement (IAVMP) problem. In addition to fully exploiting the resources of PMs, the IAVMP problem considers the quality of service (QoS) requirements of user applications and the VM interference reduction. We first formulate the IAVMP problem by an Integer Linear Programming (ILP) model to solve it optimally. Due to the computation complexity of the ILP model, we also propose a polynomial-time heuristic algorithm to efficiently solve the IAVMP problem. Finally, simulation experiments are performed to demonstrate the effectiveness of the proposed heuristic algorithm by comparing with other VM placement algorithms.
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paper_title: Server consolidation with migration control for virtualized data centers

paper_content:

Virtualization has become a key technology for simplifying service management and reducing energy costs in data centers. One of the challenges faced by data centers is to decide when, how, and which virtual machines (VMs) have to be consolidated into a single physical server. Server consolidation involves VM migration, which has a direct impact on service response time. Most of the existing solutions for server consolidation rely on eager migrations, which try to minimize the number of physical servers running VMs. These solutions generate unnecessary migrations due to unpredictable workloads that require VM resizing. This paper proposes an LP formulation and heuristics to control VM migration, which prioritize virtual machines with steady capacity. We performed experiments using TU-Berlin and Google data center workloads to compare our migration control strategy against existing eager-migration-based solutions. We observed that avoiding migration of VMs with steady capacity reduces the number of migrations with minimal penalty in the number of physical servers.
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paper_title: Inter-and-intra data center VM-placement for energy-efficient large-Scale cloud systems

paper_content:

Data centers play the crucial role in the delivery of Cloud services by enabling on-demand access to the shared resources such as software, platform, and infrastructure. Virtual Machine (VM) allocation is one of the challenging tasks in the data center management since user requirements, typically expressed as SLAs, have to be met with the minimum operational expenditure. Despite their huge processing and storage facilities, data centers are among the major contributors of the GreenHouse Gas (GHG) emissions of the IT services. In this paper, we propose a holistic approach for a large-scale Cloud system where the Cloud services are provisioned by several data centers interconnected over the backbone network. We propose a Mixed Integer Linear Programming (MILP) formulation that aims at virtualizing the backbone topology and placing the VMs in data centers with the objective of minimum power consumption. We compare our proposed solution with a benchmark MILP model which selects the closest data centers that can accommodate the user requests taking into account the CPU, memory, and bandwidth capacities of the residing physical hosts. Collected experimental results show the benefit of the proposed management scheme in terms of power consumption, resource utilization, and fairness for medium size data centers.
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paper_title: Efficient VM placement with multiple deterministic and stochastic resources in data centers

paper_content:

Virtual machines (VMs) may significantly improve the efficiency of data center infrastructure by sharing resources of physical servers. This benefit relies on an efficient VM placement scheme to minimize the number of required servers. Existing VM placement algorithms usually assume that VMs' demands for resources are deterministic and stable. However, for certain resources, such as network bandwidth, VMs' demands are bursty and time varying, and demonstrate stochastic nature. In this paper, we study efficient VM placement in data centers with multiple deterministic and stochastic resources. First, we formulate the Multidimensional Stochastic VM Placement (MSVP) problem, with the objective to minimize the number of required servers and at the same time satisfy a predefined resource availability guarantee. Then, we show that the problem is NP-hard, and propose a polynomial time algorithm called Max-Min Multidimensional Stochastic Bin Packing (M3SBP). The basic idea is to maximize the minimum utilization ratio of all the resources of a server, while satisfying the demands of VMs for both deterministic and stochastic resources. Next, we conduct simulations to evaluate the performance of M3SBP. The results demonstrate that M3SBP guarantees the availability requirement for stochastic resources, and M3SBP needs the smallest number of servers to provide the guarantee among the benchmark algorithms.
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paper_title: Data-Aware Virtual Machine Placement and Rate Allocation in Cloud Environment

paper_content:

Infrastructure-as-a-service cloud provides a suitable environment where cloud users can run data-intensive applications and store their required data files. The performance of data-intensive applications strongly depends on data transmission delay. This delay is a function of the size of data files, the location of virtual machines that run the applications as well as the allocation of data rates to virtual machines. In this paper, we propose a novel virtual machine placement algorithm that jointly optimizes the placement of virtual machines as well as their allocated data rates. Through Simulation results we show that the proposed algorithm can significantly reduce the data transfer delay for virtual machines compared to placement algorithms previously proposed in the literature.
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paper_title: Energy efficient VM placement heuristic algorithms comparison for cloud with multidimensional resources

paper_content:

Cloud computing provides user utility-oriented IT services, yet accompanied with huge energy consuming, which contributes to the high operational cost as well as CO2 emission. Making Cloud computing energy efficient can lead to a better tradeoff between profit and environmental impact. In this paper, we formulate the energy efficient VM placement problem in Cloud architecture with multidimensional resources and introduce the objective of this problem. Heuristic algorithms including three traditional local search algorithms and generic algorithm (GA) are presented to provide possible optimized solution. We conduct experiments based on Cloudsim. The result shows that GA sometimes provide the best solution, but with poor stabability. Although the BF provide neither the best nor the worst solution most of time, it have the best stabability.
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paper_title: Energy-Efficient Virtual Machine Replication and Placement in a Cloud Computing System

paper_content:

By utilizing Virtual Machines (VM) and doing server consolidation in a datacenter, a cloud provider can reduce the total energy consumption for servicing his clients with little performance degradation. In particular, the cloud provider can take advantage of dissimilar workloads and by assigning these workloads to the same server, can utilize fewer active servers to service his clients. Placing multiple copies of a VM on different servers and distributing the incoming requests among these VM copies can reduce the resource requirement for each VM copy and help the cloud provider utilize the servers more efficiently. In this paper, the problem of energy-efficient VM placement in a cloud computing system is solved. Precisely, we present an approach that first creates multiple copies of VMs and then uses dynamic programming and local search to place these copies on the physical servers. Simulation results show that the proposed algorithm reduces the total energy consumption by up to 20% with respect to previous work.
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paper_title: Optimizing VM placement for HPC in the cloud

paper_content:

Computing as a service model in cloud has encouraged High Performance Computing to reach out to wider scientific and industrial community. Many small and medium scale HPC users are exploring Infrastructure cloud as a possible platform to run their applications. However, there are gaps between the characteristic traits of an HPC application and existing cloud scheduling algorithms. In this paper, we propose an HPC-aware scheduler and implement it atop Open Stack scheduler. In particular, we introduce topology awareness and consideration for homogeneity while allocating VMs. We demonstrate the benefits of these techniques by evaluating them on a cloud setup on Open Cirrus test-bed.
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paper_title: Dynamic optimization of virtual machine placement by resource usage prediction

paper_content:

In order to save energy with preventing the lack of the resources on servers in data centers, we address the dynamic optimization of virtual machine placement. A decision problem of the virtual machine placement has been formulated as Bin Packing problem. Conventional methods repeat to solve the problem at regular intervals. Live migration is used for changing the virtual machine placement, but some of live migrations are unnecessary; the virtual machine is migrated repeatedly between the physical servers. For the purpose of reducing the unnecessary live migrations, we propose a dynamic optimization of virtual machine placement by resource usage prediction. The proposed method predicts the future resource usage by Auto Regressive Model. The proposed method decides the virtual machine placement by solving the Bin Packing problem with the predicted resource usage.
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paper_title: An Optimization-Based Scheme for Efficient Virtual Machine Placement

paper_content:

According to the important methodology of convex optimization theory, the energy-efficient and scalability problems of modern data centers are studied. Then a novel virtual machine (VM) placement scheme is proposed for solving these problems in large scale. Firstly, by referring the definition of VM placement fairness and utility function, the basic algorithm of VM placement which fulfills server constraints of physical machines is discussed. Then, we abstract the VM placement as an optimization problem which considers the inherent dependencies and traffic between VMs. By given the structural differences of recently proposed data center architectures, we further investigate a comparative analysis on the impact of the network architectures, server constraints and application dependencies on the potential performance gain of optimization-based VM placement. Comparing with the existing schemes, the performance improvements are illustrated from multiple perspectives, such as reducing the number of physical machines deployment, decreasing communication cost between VMs, improving energy-efficient and scalability of data centers.
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paper_title: Server Consolidation Algorithms with Bounded Migration Cost and Performance Guarantees in Cloud Computing

paper_content:

Consolidation of virtual machines is essential to achieve energy optimization in cloud computing environments. As virtual machines dynamically enter and leave a cloud system, it becomes necessary to relocate virtual machines among servers. However, relocation of virtual machines introduces run-time overheads and consumes extra energy, thus an careful planning for relocation is necessary. We model the relocation problem as a modified bin packing problem and propose a new server consolidation algorithm that guarantees server consolidation with {\em bounded} relocation costs. We also conduct a detailed analysis on the complexity of the server consolidation problem, and give a upper bound on the cost of relocation. Finally, we conduct simulations and compare our server consolidation algorithm with other relocation methods, like First Fit and Best Fit method. The experiment results suggest an interesting trade-off between server consolidation quality and relocation cost. Our algorithm is able to trade about 1\% in server consolidation quality for a reduction about 50\% in relocation cost, when compared with other well known bin packing algorithms. We also note that the relocation cost incurred in our method is much less than the theoretical bound we provided. The reason is that we overestimate the amount of relocation from theoretical analysis, and the actual amount of relocation found from experiments is much less than the worst-case bound from theoretical analysis.
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paper_title: Multi-objective Virtual Machine Placement with Service Level Agreement: A Memetic Algorithm Approach

paper_content:

The process of selecting which virtual machines should be located (i.e. executed) at each physical machine of a Datacenter is known as Virtual Machine Placement VMP. This work proposes for the first time a multi-objective formulation of the VMP considering Service Level Agreement. A novel multiobjective memetic algorithm is also proposed to solve the formulated multi-objective problem. This proposal is validated comparing experimental results of the proposed algorithm with a brute force exhaustive search algorithm. Simulations prove the correctness of the proposed memetic algorithm and its scalability considering different experimental scenarios.
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paper_title: Reducing electricity cost through virtual machine placement in high performance computing clouds

paper_content:

In this paper, we first study the impact of load placement policies on cooling and maximum data center temperatures in cloud service providers that operate multiple geographically distributed data centers. Based on this study, we then propose dynamic load distribution policies that consider all electricity-related costs as well as transient cooling effects. Our evaluation studies the ability of different cooling strategies to handle load spikes, compares the behaviors of our dynamic cost-aware policies to cost-unaware and static policies, and explores the effects of many parameter settings. Among other interesting results, we demonstrate that (1) our policies can provide large cost savings, (2) load migration enables savings in many scenarios, and (3) all electricity-related costs must be considered at the same time for higher and consistent cost savings.
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paper_title: An energy-efficient approach for virtual machine placement in cloud based data centers

paper_content:

Cloud computing is a new technology which is proffering IT services based on pay-as-you-go model to consumers from everywhere in the world. The growing demand of Cloud infrastructure and modern computational requests like business, scientific and web applications result in large-scale data centers and lead to extra electrical energy consumption. High energy consumption causes high operational cost and also led to high carbon emission which is harmful for atmosphere. Hence, energy-efficient techniques are required to minimize the negative effects of Cloud computing on the environment. Virtual machines (VMs) migration, dynamic consolidation in the virtualized data centers in cloud environments and switching idle physical machines off could yield reduce energy consumption; hence, one of the important issues for energy efficiency in virtualized cloud environments is how to place new VMs or selected VMs for migration across the hosts. In this paper we propose an energy-efficient approach based on Minimum Correlation Coefficient (MCC) method for virtual machine placement in cloud based, virtualized data centers. The proposed approach regards both Service Level Agreement (SLA) and low energy consumption and tries to make a trade-off between these two concerns using fuzzy Analytic Hierarchy Process (AHP). We evaluate our approach using Cloudsim toolkit as a modern cloud computing environment simulator. The evaluation shows that our proposed method offers a suitable trade-off between power efficiency and SLA violation reduction in cloud data centers.
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paper_title: Towards Profitable Virtual Machine Placement in the Data Center

paper_content:

Motivated by the limit on the power usage effectiveness (PUE) of the data centers, the potential benefit of the consolidation, and the impetus of achieving maximum return on investment (ROI) on the cloud computing market, we investigate VM placement in the data center, formulate a multi-level generalized assignment problem (MGAP) for maximizing the profit under the service level agreement and the power budget constraint based on the model of a virtualized data center, and solve it with a first-fit heuristic. Numerical simulations show that the first-fit heuristic is effective in solving the large-scale instances of the MGAP with the sampled simulation setups.
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paper_title: SLA guaranteed virtual machine consolidation for computing clouds

paper_content:

One of the most attractive features that computing clouds can offer is that the data center maintenance cost can be reduced by consolidating the workload generated by the virtual machines (VMs) into a few powerful physical servers. In this paper, the problem of maximally consolidating heterogeneous virtual machines into servers while protecting the service level agreement of each virtual machine is investigated. A generic and robust VM workload consolidation framework is proposed. The proposed framework achieves several attractive properties like low maintenance cost, low algorithm complexity, adjustable resource provisioning aggressiveness, and good scalability.
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paper_title: Time-Aware VM-Placement and Routing with Bandwidth Guarantees in Green Cloud Data Centers

paper_content:

Variation in network performance due to the shared resources is a key obstacle for cloud adoption. Thus, the success of cloud providers to attract more tenants depends on their ability to provide bandwidth guarantees. Power efficiency in data centers has become critically important for supporting larger number of tenants. In this paper, we address the problem of time-aware VM-placement and routing (TVPR), where each tenant requests for a specified amount of server resources (VMs) and network resource (bandwidth) for a given duration. The TVPR problem allocates the required resources for as many tenants as possible by finding the right set of servers to map their VMs and routing their traffic so as to minimize the total power consumption. We propose a multi-component utilization-based power model to determine the total power consumption of a data center according to the resource utilization of the components (servers and switches). We then develop a mixed integer linear programming (MILP) optimization problem formulation based on the proposed power model and prove it to be N P-complete. Since the TVPR problem is computationally prohibitive, we develop a fast and scalable heuristic algorithm. To demonstrate the efficiency of our proposed algorithm, we compare its performance with the numerical results obtained by solving the MILP problem using CPLEX, for a small data center. We then demonstrate the effectiveness of the proposed algorithm in terms of power consumption and acceptance ratio for large data centers through simulation results.
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paper_title: Optimal virtual machine placement across multiple cloud providers

paper_content:

Cloud computing provides users an efficient way to dynamically allocate computing resources to meet demands. Cloud providers can offer users two payment plans, i.e., reservation and on-demand plans for resource provisioning. Price of resources in reservation plan is generally cheaper than that in on-demand plan. However, since the reservation plan has to be acquired in advance, it may not fully meet future demands in which the on-demand plan can be used to guarantee the availability to the user. In this paper, we propose an optimal virtual machine placement (OVMP) algorithm. This algorithm can minimize the cost spending in each plan for hosting virtual machines in a multiple cloud provider environment under future demand and price uncertainty. OVMP algorithm makes a decision based on the optimal solution of stochastic integer programming (SIP) to rent resources from cloud providers. The performance of OVMP algorithm is evaluated by numerical studies and simulation. The results clearly show that the proposed OVMP algorithm can minimize users' budgets. This algorithm can be applied to provision resources in emerging cloud computing environments.
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paper_title: Higher SLA satisfaction in datacenters with continuous VM placement constraints

paper_content:

In a virtualized datacenter, the Service Level Agreement for an application restricts the Virtual Machines (VMs) placement. An algorithm is in charge of maintaining a placement compatible with the stated constraints. Conventionally, when a placement algorithm computes a schedule of actions to re-arrange the VMs, the constraints ignore the intermediate states of the datacenter to only restrict the resulting placement. This situation may lead to temporary violations of the constraints. In this paper, we discuss the causes of these violations. We then advocate for continuous placement constraints to restrict also the actions schedule. We discuss why their development requires more attention and how the extensible placement algorithm BtrPlace can address this issue.
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paper_title: A Mathematical Programming Approach for Server Consolidation Problems in Virtualized Data Centers

paper_content:

Today's data centers offer IT services mostly hosted on dedicated physical servers. Server virtualization provides a technical means for server consolidation. Thus, multiple virtual servers can be hosted on a single server. Server consolidation describes the process of combining the workloads of several different servers on a set of target servers. We focus on server consolidation with dozens or hundreds of servers, which can be regularly found in enterprise data centers. Cost saving is among the key drivers for such projects. This paper presents decision models to optimally allocate source servers to physical target servers while considering real-world constraints. Our central model is proven to be an NP-hard problem. Therefore, besides an exact solution method, a heuristic is presented to address large-scale server consolidation projects. In addition, a preprocessing method for server load data is introduced allowing for the consideration of quality-of-service levels. Extensive experiments were conducted based on a large set of server load data from a data center provider focusing on managerial concerns over what types of problems can be solved. Results show that, on average, server savings of 31 percent can be achieved only by taking cycles in the server workload into account.
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paper_title: A virtual machine consolidation framework for MapReduce enabled computing clouds

paper_content:

In nowadays computing clouds, it is of the cloud providers' economic interests to correctly consolidate the workload of the virtual machines (VMs) into the suitable physical servers in the cloud data center in order to minimize the total maintenance cost. However, during the consolidation process, sufficient protection should be provided to the service level agreement (SLA) of the VMs. In this paper, the VM consolidation problem for MapReduce enabled computing clouds has been investigated. In the MapReduce enabled computing clouds, MapReduce jobs are carried out by homogeneous MapReduce VM instances that have identical hardware resource. Two resource allocation schemes with corresponding SLA constraints for the MapReduce VMs and the non-MapReduce VMs are proposed. Based on these schemes, the VM consolidation problem is modeled as an integer nonlinear optimization problem and an efficient algorithm has been proposed to locate its solutions. The results show that better VM consolidation performance can be achieved by colocating MapReduce instances together with non-MapReduce instances in the same set of physical servers.
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paper_title: Optimal update frequency model for physical machine state change and virtual machine placement in the cloud

paper_content:

Cloud computing is evolving into the default operational framework running modern data centers. Efficient data center operation is concerned with the total amount of energy consumed as well as assuring adequate resources are available to process all of the incoming work requests. Existing research has demonstrated several algorithms that can be used to determine the optimal number of resources required to service these requests. However, a key issue not addressed in these algorithms is determining the frequency of recalculating the number of required resources. Changing the required resources at a rate slower than the optimal update frequency results in lower energy efficiency due to the over allocation of resources. Changing the resources at a rate higher than the optimal frequency results in insufficient time for systems to change state, which results in SLA violations. In this paper, a stochastic optimization model is presented that determines the optimal update frequency for changing the states of the nodes of the cloud as well as determining the proper frequency for recalculating the maximum expected load, which improves the determination of the optimum number of resources required, therefore maximizes energy efficiency and minimizes SLA violations.
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paper_title: Energy Efficient VM Placement Supported by Data Analytic Service

paper_content:

The popularity and commercial use of cloud computing has prompted an increased concern among cloud service providers for energy efficiency while still maintaining quality of service. One of the key techniques used for the efficient use of cloud server resources is virtual machine placement. This work introduces a precise VM placement algorithm that ensures energy efficiency and also prevents Service Level Agreement (SLA) violation. The mathematical model of the algorithm is supported by a sophisticated data analytic system implemented as a service. The precision of the algorithm is achieved by allowing each individual VM to build its own data model on demand over an appropriate time horizon. Thus the data model can reflect accurately the characteristics of resource usage of the VM. The algorithm can communicate synchronously or asynchronously with the data analytic service which is deployed as a cloud-based solution. In the experiments, several advanced data modelling and use forecasting techniques were evaluated. Results from simulation-based experiments show that the VM placement algorithm (supported by the data analytic service) can effectively reduce power consumption, the number of VM migrations, and prevent SLA violation, it also compares very favourably with other placement algorithms.
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paper_title: Evolutionary Optimal Virtual Machine Placement and Demand Forecaster for Cloud Computing

paper_content:

Cloud computing allows the users to efficiently and dynamically provision computing resources to meet their IT needs. Most cloud providers offer two types of payment plans to the user, i.e., reservation and on-demand. The reservation plan is typically cheaper than the on-demand plan but reservation plan has to be provisioned in advance. Reserving the resources would be straightforward if the actual computing demand (e.g., job processing) is known in advance. However, in reality, the actual computing demand can be observed only at the point of actual usage. Therefore, it is difficult to reserve the correct amount of resources during the reservation to meet the computing demands of the users. In this paper, we propose an evolutionary optimal virtual machine placement (EOVMP) algorithm with a demand forecaster. First, a demand forecaster predicts the computing demand. Then, EOVMP uses this predicted demand to allocate the virtual machines using reservation and on-demand plans for job processing. The performance of the proposed schemes is evaluated by simulations and numerical studies. The evaluation result shows that the EOVMP algorithm can provide the solution close to the optimal solution of stochastic integer programming (SIP) and the prediction of the demand forecaster is of reasonable accuracy.
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paper_title: Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments

paper_content:

Server consolidation using virtualization technology has become increasingly important for improving data center efficiency. It enables one physical server to host multiple independent virtual machines (VMs), and the transparent movement of workloads from one server to another. Fine-grained virtual machine resource allocation and reallocation are possible in order to meet the performance targets of applications running on virtual machines. On the other hand, these capabilities create demands on system management, especially for large-scale data centers. In this paper, a two-level control system is proposed to manage the mappings of workloads to VMs and VMs to physical resources. The focus is on the VM placement problem which is posed as a multi-objective optimization problem of simultaneously minimizing total resource wastage, power consumption and thermal dissipation costs. An improved genetic algorithm with fuzzy multi-objective evaluation is proposed for efficiently searching the large solution space and conveniently combining possibly conflicting objectives. The simulation-based evaluation using power-consumption and thermal-dissipation models based on profiling of a Blade Center, demonstrates the good performance, scalability and robustness of our proposed approach. Compared with four well-known bin-packing algorithms and two single-objective approaches, the solutions obtained from our approach seek good balance among the conflicting objectives while others cannot.
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paper_title: Dynamic Energy-Efficient Virtual Machine Placement Optimization for Virtualized Clouds

paper_content:

A virtual machine placement strategy based on the trade-off between energy consumption and SLA is presented. Aiming at dynamical changes of workload requirements, a self-adaptive placement strategy RLWR based on robust local weight regression is presented, which could decide the overload time of hosts dynamically. After detecting overloaded hosts, one virtual machine migration selection algorithm MNM is proposed. The MNM’s objective is to get minimal migration number. The migrated virtual machines are deployed using bin-packing algorithm PBFDH. The experimental results show that our algorithm has obvious advantages than other algorithms.
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paper_title: Provisioning of requests for virtual machine sets with placement constraints in IaaS clouds

paper_content:

We study the problem of optimising the provisioning of collections of virtual machines (VMs) having different placement constraints (e.g., security and anti-collocation) and characteristics (e.g., memory and disk capacity), given a set of physical machines (PMs) with known specifications, in order to achieve the objective of maximising an IaaS cloud provider's revenue. We propose two approaches. The first is based on the formulation of the problem as an integer linear programming (ILP) problem, the solution to which provides an optimal VM placement. The second approach is a heuristic based on classifying the requests into different categories and satisfying the constraints in a particular order using a first lit decreasing (FFD) algorithm for multi-dimensional vector bin packing problem. Given a model of VM placement constraints, offered resources and requests with multiple VM types, both approaches devise a placement plan in a way that maximizes revenue, having due regard both to customer requirements and PM capacities. We evaluate the relative performance of the solutions by means of numerical experiments. The results suggest the optimal solution is not practical for medium to large problems, but it is encouraging that the placement plans of the heuristic are close to those of the optimal solution for smaller problem sizes. We use the heuristic to generate results for large scale placement problems; experiments suggest that it is practical in terms of its runtime efficiency and can provide an effective means of online VM-to-PM mapping.
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paper_title: Multi-objective Virtual Machine Placement with Service Level Agreement: A Memetic Algorithm Approach

paper_content:

The process of selecting which virtual machines should be located (i.e. executed) at each physical machine of a Datacenter is known as Virtual Machine Placement VMP. This work proposes for the first time a multi-objective formulation of the VMP considering Service Level Agreement. A novel multiobjective memetic algorithm is also proposed to solve the formulated multi-objective problem. This proposal is validated comparing experimental results of the proposed algorithm with a brute force exhaustive search algorithm. Simulations prove the correctness of the proposed memetic algorithm and its scalability considering different experimental scenarios.
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paper_title: Improved energy-efficiency in cloud datacenters with interference-aware virtual machine placement

paper_content:

Virtualization is one of the main technologies used for improving resource efficiency in datacenters; it allows the deployment of co-existing computing environments over the same hardware infrastructure. However, the co-existing of environments — along with management inefficiencies — often creates scenarios of high-competition for resources between running workloads, leading to performance degradation. This phenomenon is known as Performance Interference, and introduces a non-negligible overhead that affects both a datacenter's Quality of Service and its energy-efficiency. This paper introduces a novel approach to workload allocation that improves energy-efficiency in Cloud datacenters by taking into account their workload heterogeneity. We analyze the impact of performance interference on energy-efficiency using workload characteristics identified from a real Cloud environment, and develop a model that implements various decision-making techniques intelligently to select the best workload host according to its internal interference level. Our experimental results show reductions in interference by 27.5% and increased energy-efficiency up to 15% in contrast to current mechanisms for workload allocation.
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paper_title: Particle Swarm Optimization for Energy-Aware Virtual Machine Placement Optimization in Virtualized Data Centers

paper_content:

A critical research issue is to lower the energy consumption of a virtualized data center by means of virtual machine placement optimization while satisfying the resource requirements of the cloud services. In this paper, we focus on different existing schemes and on the energy-aware virtual machine placement optimization problem of a heterogeneous virtualized data center. We attempt to explore a better alternative approach to minimizing the energy consumption, and we observe that particle swarm optimization (PSO) has considerable potential. However, the PSO must be improved to solve an optimization problem. The improvement includes redefining the parameters and operators of the PSO, adopting an energy-aware local fitness first strategy and designing a novel coding scheme. Using the improved PSO, an optimal virtual machine replacement scheme with the lowest energy consumption can be found. Experimental results indicate that our approach significantly outperforms other approaches, and can lessen 13%-23% energy consumption in the context of this paper.
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paper_title: Intelligent virtual machine placement for cost efficiency in geo-distributed cloud systems

paper_content:

An important challenge of running large-scale cloud services in a geo-distributed cloud system is to minimize the overall operating cost. The operating cost of such a system includes two major components: electricity cost and wide-area-network (WAN) communication cost. While the WAN communication cost is minimized when all virtual machines (VMs) are placed in one datacenter, the high workload at one location requires extra power for cooling facility and results in worse power usage effectiveness (PUE). In this paper, we develop a model to capture the intrinsic trade-off between electricity and WAN communication costs, and formulate the optimal VM placement problem, which is NP-hard due to its binary and quadratic nature. While exhaustive search is not feasible for large-scale scenarios, heuristics which only minimize one of the two cost terms yield less optimized results. We propose a cost-aware two-phase metaheuristic algorithm, Cut-and-Search, that approximates the best trade-off point between the two cost terms. We evaluate Cut-and-Search by simulating it over multiple cloud service patterns. The results show that the operating cost has great potential of improvement via optimal VM placement. Cut-and-Search achieves a highly optimized trade-off point within reasonable computation time, and outperforms random placement by 50%, and the partial-optimizing heuristics by 10-20%.
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paper_title: Energy-aware virtual machine placement in data centers

paper_content:

The energy efficiency of modern data centers has become a practical concern and has attracted significant attention in recent years. In contract to existing solutions that primarily focuses on only one specific aspect of management to reduce energy consumption, this paper explores the balance between server energy consumption and network energy consumption to present an energy-aware joint virtual machine (VM) placement. Given the definition of VM placement fairness, the basic algorithm of VM placement which fulfills server energy consumption constraints is conducted. Then, we further formulate the VM placement as an optimization problem which considers application dependencies to reduce network energy consumption. We design a joint algorithm that efficiently solves the VM placement problem for very large problem sizes. Using simulations, we conduct a comparative analysis on the impact of the data center architectures, server constraints and application dependencies on the potential performance gain of energy-aware VM placement. Compared to existing generic methods, we show a significant performance improvement such as efficiently reducing the number of physical machines used to save server energy consumption, decreasing the communication distance between VMs to obtain data center network energy consumption efficiency, improving scalability of data centers.
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paper_title: A network-aware virtual machine placement algorithm in mobile cloud computing environment

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By taking into consideration network features of a wireless mesh network environment where mobile cloud computing (MCC) system is deployed, this paper proposes an effective virtual machine (VM) placement algorithm. This algorithm tends to place a newly created VM for a cloud service to a cloud server, which itself is also a mobile device, that occurs lowest network delay when accessing to application data stored in wireline networks. The aim is to reduce cloud service response time as much as possible in order to deliver better quality of experience to cloud end users over more noisy and less robust wireless networks. In addition, the proposed deployment of cloud storage and cloud computational resources onto wireless networks is also a contribution of the paper. The simulation results have shown the effectiveness and the efficiency of the proposed VM placement algorithms both in terms of cloud service response time.
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paper_title: A Hybrid Genetic Algorithm for the Energy-Efficient Virtual Machine Placement Problem in Data Centers

paper_content:

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation technology. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches consider the energy consumption by physical machines only, but do not consider the energy consumption in communication network, in a data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement. In our preliminary research, we have proposed a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both physical machines and the communication network in a data center. Aiming at improving the performance and efficiency of the genetic algorithm, this paper presents a hybrid genetic algorithm for the energy-efficient virtual machine placement problem. Experimental results show that the hybrid genetic algorithm significantly outperforms the original genetic algorithm, and that the hybrid genetic algorithm is scalable.
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paper_title: An Energy Efficient Virtual Machine Placement Algorithm with Balanced Resource Utilization

paper_content:

Virtualization is a key technology for resource sharing in IaaS/PaaS cloud infrastructures. One primary issue in virtualization is the virtual machine placement (VMP) problem, which is to choose proper physical machine (PM) to deploy virtual machines (VMs) in runtime. In this paper, we study the VMP problem with the goal of minimizing the total energy consumption. We first present a multi-dimensional space partition model to characterize the resource usage states of PMs. Based on this model, we then propose a virtual machine placement algorithm, which can balance the utilization of multi-dimensional resources, reduce the number of running PMs and thus lower down the energy consumption. We also evaluate our proposed balanced algorithm via extensive simulations. Simulation results show that our approach can save as much as 15% energy compared to the first fit algorithm over a long run.
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paper_title: Reducing electricity cost through virtual machine placement in high performance computing clouds

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In this paper, we first study the impact of load placement policies on cooling and maximum data center temperatures in cloud service providers that operate multiple geographically distributed data centers. Based on this study, we then propose dynamic load distribution policies that consider all electricity-related costs as well as transient cooling effects. Our evaluation studies the ability of different cooling strategies to handle load spikes, compares the behaviors of our dynamic cost-aware policies to cost-unaware and static policies, and explores the effects of many parameter settings. Among other interesting results, we demonstrate that (1) our policies can provide large cost savings, (2) load migration enables savings in many scenarios, and (3) all electricity-related costs must be considered at the same time for higher and consistent cost savings.
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paper_title: An energy-efficient approach for virtual machine placement in cloud based data centers

paper_content:

Cloud computing is a new technology which is proffering IT services based on pay-as-you-go model to consumers from everywhere in the world. The growing demand of Cloud infrastructure and modern computational requests like business, scientific and web applications result in large-scale data centers and lead to extra electrical energy consumption. High energy consumption causes high operational cost and also led to high carbon emission which is harmful for atmosphere. Hence, energy-efficient techniques are required to minimize the negative effects of Cloud computing on the environment. Virtual machines (VMs) migration, dynamic consolidation in the virtualized data centers in cloud environments and switching idle physical machines off could yield reduce energy consumption; hence, one of the important issues for energy efficiency in virtualized cloud environments is how to place new VMs or selected VMs for migration across the hosts. In this paper we propose an energy-efficient approach based on Minimum Correlation Coefficient (MCC) method for virtual machine placement in cloud based, virtualized data centers. The proposed approach regards both Service Level Agreement (SLA) and low energy consumption and tries to make a trade-off between these two concerns using fuzzy Analytic Hierarchy Process (AHP). We evaluate our approach using Cloudsim toolkit as a modern cloud computing environment simulator. The evaluation shows that our proposed method offers a suitable trade-off between power efficiency and SLA violation reduction in cloud data centers.
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paper_title: Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement

paper_content:

The scalability of modern data centers has become a practical concern and has attracted significant attention in recent years. In contrast to existing solutions that require changes in the network architecture and the routing protocols, this paper proposes using traffic-aware virtual machine (VM) placement to improve the network scalability. By optimizing the placement of VMs on host machines, traffic patterns among VMs can be better aligned with the communication distance between them, e.g. VMs with large mutual bandwidth usage are assigned to host machines in close proximity. We formulate the VM placement as an optimization problem and prove its hardness. We design a two-tier approximate algorithm that efficiently solves the VM placement problem for very large problem sizes. Given the significant difference in the traffic patterns seen in current data centers and the structural differences of the recently proposed data center architectures, we further conduct a comparative analysis on the impact of the traffic patterns and the network architectures on the potential performance gain of traffic-aware VM placement. We use traffic traces collected from production data centers to evaluate our proposed VM placement algorithm, and we show a significant performance improvement compared to existing general methods that do not take advantage of traffic patterns and data center network characteristics.
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paper_title: Joint VM placement and routing for data center traffic engineering

paper_content:

Today's data centers need efficient traffic management to improve resource utilization in their networks. In this work, we study a joint tenant (e.g., server or virtual machine) placement and routing problem to minimize traffic costs. These two complementary degrees of freedom—placement and routing—are mutually-dependent, however, are often optimized separately in today's data centers. Leveraging and expanding the technique of Markov approximation, we propose an efficient online algorithm in a dynamic environment under changing traffic loads. The algorithm requires a very small number of virtual machine migrations and is easy to implement in practice. Performance evaluation that employs the real data center traffic traces under a spectrum of elephant and mice flows, demonstrates a consistent and significant improvement over the benchmark achieved by common heuristics.
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paper_title: VMPlanner : Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers

paper_content:

In recent years, the power costs of cloud data centers have become a practical concern and have attracted significant attention from both industry and academia. Most of the early works on data center energy efficiency have focused on the biggest power consumers (i.e., computer servers and cooling systems), yet without taking the networking part into consideration. However, recent studies have revealed that the network elements consume 10-20% of the total power in the data center, which poses a great challenge to effectively reducing network power cost without adversely affecting overall network performance. Based on the analysis on topology characteristics and traffic patterns of data centers, this paper presents a novel approach, called VMPlanner, for network power reduction in the virtualization-based data centers. The basic idea of VMPlanner is to optimize both virtual machine placement and traffic flow routing so as to turn off as many unneeded network elements as possible for power saving. We formulate the optimization problem, analyze its hardness, and solve it by designing VMPlanner as a stepwise optimization approach with three approximation algorithms. VMPlanner is implemented and evaluated in a simulated environment with traffic traces collected from a data center test-bed, and the experiment results illustrate the efficacy and efficiency of this approach.
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paper_title: Towards Profitable Virtual Machine Placement in the Data Center

paper_content:

Motivated by the limit on the power usage effectiveness (PUE) of the data centers, the potential benefit of the consolidation, and the impetus of achieving maximum return on investment (ROI) on the cloud computing market, we investigate VM placement in the data center, formulate a multi-level generalized assignment problem (MGAP) for maximizing the profit under the service level agreement and the power budget constraint based on the model of a virtualized data center, and solve it with a first-fit heuristic. Numerical simulations show that the first-fit heuristic is effective in solving the large-scale instances of the MGAP with the sampled simulation setups.
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paper_title: Multiobjective Virtual Machine Placement in Cloud Environment

paper_content:

In this paper, the virtual machine placement problem is formulated as a multi-objective optimization problem. The objectives are maximizing profit, maximizing load balancing and minimizing recourse wastage. Results of Genetic algorithms, Non-dominated Sorting Genetic Algorithm and Non-dominated Sorting Genetic Algorithm-II are compared with common solution representation, penalty and benefit values. All the three algorithms reported good solutions whereas GA and NSGA are subjected to premature convergence and duplicate solutions. NSGA-II gives good and diversified range of solutions.
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paper_title: SLA guaranteed virtual machine consolidation for computing clouds

paper_content:

One of the most attractive features that computing clouds can offer is that the data center maintenance cost can be reduced by consolidating the workload generated by the virtual machines (VMs) into a few powerful physical servers. In this paper, the problem of maximally consolidating heterogeneous virtual machines into servers while protecting the service level agreement of each virtual machine is investigated. A generic and robust VM workload consolidation framework is proposed. The proposed framework achieves several attractive properties like low maintenance cost, low algorithm complexity, adjustable resource provisioning aggressiveness, and good scalability.
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paper_title: Prevent VM Migration in Virtualized Clusters via Deadline Driven Placement Policy

paper_content:

VM consolidation has been shown as a promising technique for saving energy costs of a data center. It relies on VM migration to move user applications or jobs onto fewer numbers of physical servers during off peak hour. However, VM migration is a costly operation that could cause several concerns, such as performance degradation and system instability. Most existing works were proposed to minimize the migration cost for dynamic consolidation which migrates VM at the runtime when SLA violation or resource under-utilization is detected. In contrast, this paper aims to proactively prevent VM migration for semi-static VM consolidation by proposing a deadline driven VM placement strategy based on the awareness of the server turn-off time and job execution time. We evaluate our approach using a real HPC cluster trace as well as a set of synthetic generated workloads. The results show our approach can significantly reduce the number of migrations by 70% on the real trace. We also demonstrate that our approach can be resilient to different workload patterns by achieving consistent improvement around 50% over all the synthetic workloads.
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paper_title: A Two Stage Heuristic Algorithm for Solving the Server Consolidation Problem with Item-Item and Bin-Item Incompatibility Constraints

paper_content:

The problem of server sprawl is common in data centers of most business organizations. It is most often the case that an application is run on dedicated servers. This leads to situations where organizations end up having numerous servers that remain under-utilized most of the times. The servers, in such scenarios, are allocated more resources (disk, cpu and memory) than are justified by their present workloads. Consolidating multiple underutilized servers into a fewer number of non-dedicated servers that can host multiple applications is an effective tool for businesses to enhance their returns on investment. The problem can be modeled as a variant of the bin packing problem where items to be packed are the servers being consolidated and bins are the target servers. The sizes of the servers/items being packed are resource utilizations which are obtained from the performance trace data. Here we describe a novel two stage heuristic algorithm for taking care of the "bin-item" incompatibility constraints that are inherent in any server consolidation problem. The model is able to solve extremely large instances of problem in a reasonable amount of time.
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paper_title: On Modelling Virtual Machine Consolidation to Pseudo-Boolean Constraints

paper_content:

Cloud Computing is a new paradigm of distributed computing that offers virtualized resources and services over the Internet. To offer Infrastructure-as-a-Service (IaaS) many Cloud providers uses a large data center which usage ranges 5% to 10% of capacity in average. In order to improve Cloud data center management and resources usage a Virtual Machine (VM) consolidation technique can be applied to increase workloads and save energy. Using VM consolidation, we introduce an artificial intelligence consolidation based in Pseudo-Boolean (PB) Constraints to find a optimal consolidation. To evaluate our PB consolidation approach we used the DInf-UFPR and Google Cluster scenario and the formulas are solved with two state-of-the-art solvers.
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paper_title: Time-Aware VM-Placement and Routing with Bandwidth Guarantees in Green Cloud Data Centers

paper_content:

Variation in network performance due to the shared resources is a key obstacle for cloud adoption. Thus, the success of cloud providers to attract more tenants depends on their ability to provide bandwidth guarantees. Power efficiency in data centers has become critically important for supporting larger number of tenants. In this paper, we address the problem of time-aware VM-placement and routing (TVPR), where each tenant requests for a specified amount of server resources (VMs) and network resource (bandwidth) for a given duration. The TVPR problem allocates the required resources for as many tenants as possible by finding the right set of servers to map their VMs and routing their traffic so as to minimize the total power consumption. We propose a multi-component utilization-based power model to determine the total power consumption of a data center according to the resource utilization of the components (servers and switches). We then develop a mixed integer linear programming (MILP) optimization problem formulation based on the proposed power model and prove it to be N P-complete. Since the TVPR problem is computationally prohibitive, we develop a fast and scalable heuristic algorithm. To demonstrate the efficiency of our proposed algorithm, we compare its performance with the numerical results obtained by solving the MILP problem using CPLEX, for a small data center. We then demonstrate the effectiveness of the proposed algorithm in terms of power consumption and acceptance ratio for large data centers through simulation results.
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paper_title: Online traffic-aware virtual machine placement in data center networks

paper_content:

Data centers rely on virtualization to provide different services over a shared infrastructure. The placement of the different services and tasks in the physical machines is crucial for the performance of the whole system. A misplaced service can overload some network links, lead to congestion, or even connection disruptions. On the other hand, virtual machine migration allows reallocating services and changing the traffic matrix, leading to more efficient use of bandwidth. In this paper, we propose a Virtual Machine Placement (VMP) algorithm to (re)allocate virtual machines in the data center servers, based on the current traffic matrix, CPU, and memory usage. Analyzing the formation of community patterns in terms of traffic using graph theory, we are able to find virtual machines that are correlated because they exchange high amount of data. Those virtual machines are aggregated and allocated to servers as close as possible to each other, reducing traffic congestion. Our simulation results show that VMP was able to improve the traffic distribution. In some specific cases we were able to reduce 80% of the core traffic, concentrating it at the edge of the network.
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paper_title: A simulated annealing algorithm for energy efficient virtual machine placement

paper_content:

Improving energy efficiency has become increasingly important in data centers in recent years to reduce the rapidly growing tremendous amounts of electricity consumption. The power dissipation of the physical servers is the root cause of power usage of other systems, such as cooling systems. Many efforts have been made to make data centers more energy efficient. One of them is to minimize the total power consumption of these servers in a data center through virtual machine consolidation, which is implemented by virtual machine placement. The placement problem is often modeled as a bin packing problem. Due to the NP-hard nature of the problem, heuristic solutions such as First Fit and Best Fit algorithms have been often used and have generally good results. However, their performance leaves room for further improvement. In this paper we propose a Simulated Annealing based algorithm, which aims at further improvement from any feasible placement. This is the first published attempt of using SA to solve the VM placement problem to optimize the power consumption. Experimental results show that this SA algorithm can generate better results, saving up to 25 percentage more energy than First Fit Decreasing in an acceptable time frame.
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paper_title: Virtual Machine Placement Optimization Supporting Performance SLAs

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Cloud computing model separates usage from ownership in terms of control on resource provisioning. Resources in the cloud are projected as a service and are realized using various service models like IaaS, PaaS and SaaS. In IaaS model, end users get to use a VM whose capacity they can specify but not the placement on a specific host or with which other VMs it can be co-hosted. Typically, the placement decisions happen based on the goals like minimizing the number of physical hosts to support a given set of VMs by satisfying each VMs capacity requirement. However, the role of the VMM usage to support I/O specific workloads inside a VM can make this capacity requirement incomplete. I/O workloads inside VMs require substantial VMM CPU cycles to support their performance. As a result, placement algorithms need to include the VMM's usage on a per VM basis. Secondly, cloud centers encounter situations wherein change in existing VM's capacity or launching of new VMs need to be considered during different placement intervals. Usually, this change is handled by migrating existing VMs to meet the goal of optimal placement. We argue that VM migration is not a trivial task and does include loss of performance during migration. We quantify this migration overhead based on the VM's workload type and include the same in placement problem. One of the goals of the placement algorithm is to reduce the VM's migration prospects, thereby reducing chances of performance loss during migration. This paper evaluates the existing ILP and First Fit Decreasing (FFD) algorithms to consider these constraints to arrive at placement decisions. We observe that ILP algorithm yields optimal results but needs long computing time even with parallel version. However, FFD heuristics are much faster and scalable algorithms that generate a sub-optimal solution, as compared to ILP, but in time-scales that are useful in real-time decision making. We also observe that including VM migration overheads in the placement algorithm results in a marginal increase in the number of physical hosts but a significant, of about 84 percent reduction in VM migration.
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paper_title: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing

paper_content:

Virtual machine placement is a process of mapping virtual machines to physical machines. The optimal placement is important for improving power efficiency and resource utilization in a cloud computing environment. In this paper, we propose a multi-objective ant colony system algorithm for the virtual machine placement problem. The goal is to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption. The proposed algorithm is tested with some instances from the literature. Its solution performance is compared to that of an existing multi-objective genetic algorithm and two single-objective algorithms, a well-known bin-packing algorithm and a max-min ant system (MMAS) algorithm. The results show that the proposed algorithm is more efficient and effective than the methods we compared it to.
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paper_title: Design and Implementation of a Multi-objective Optimization Mechanism for Virtual Machine Placement in Cloud Computing Data Center

paper_content:

Cloud computing is becoming a popular way of supplying and using computing resources. A cloud-computing data center is equipped with a large number of physical resources and must manage an even larger number of virtual machines (VMs). The center’s VM placement strategy affects the utilization of physical resources, and consequently, it influences operational costs. Our goal is to develop a multi-objective optimization mechanism for VM placement that satisfies various constraints and results in the lowest operational cost. The number of possible combinations of VMs and hosts can be extremely large. For the mechanism to be practical, the number of possible combinations must be reduced. We reduced computational overheads by classifying VM hosts into a relatively small number of equivalent sets. Simulation results show that expected operational costs can be significantly reduced by applying the proposed mechanism.
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paper_title: An Availability-Aware Virtual Machine Placement Approach for Dynamic Scaling of Cloud Applications

paper_content:

Cloud computing promises customers the on-demand ability to dynamically provision virtualization resources in face of workload variations. Most existing scaling approaches addressed this problem by allocating application to a certain amount of cloud resources. However, the problem of the availability of application influenced by VM-based physical locations during resource scaling process is a serious challenge due to dynamic complex workload and has not been widely discussed yet. In this paper, we present a novel availability-based computing model to describe availability attribute of one application in the hierarchical structured cloud. Moreover, we propose an availability-aware policy by performing both vertical and horizontal scaling to explore how and where to allocate computing resource. Simulation results indicate that our model captured the availability of cloud applications properly and proposed scaling approach achieves the objectives of meeting availability demands and minimizing the total communication cost.
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paper_title: Improving Performance and Availability of Services Hosted on IaaS Clouds with Structural Constraint-Aware Virtual Machine Placement

paper_content:

The increasing popularity of modern virtualization-based datacenters continues to motivate both industry and academia to provide answers to a large variety of new and challenging questions. In this paper we aim to answer focusing on one such question: how to improve performance and availability of services hosted on IaaS clouds. Our system, structural constraint-aware virtual machine placement (SCAVP), supports three types of constraints: demand, communication and availability. We formulate SCAVP as an optimization problem and show its hardness. We design a hierarchical placement approach with four approximation algorithms that efficiently solves the SCAVP problem for large problem sizes. We provide a formal model for the application (to better understand structural constraints) and the datacenter (to effectively capture capabilities), and use the two models as inputs to the placement problem. We evaluate SCAVP in a simulated environment to illustrate the efficiency and importance of the proposed approach.
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paper_title: An energy-aware heuristic framework for virtual machine consolidation in Cloud computing

paper_content:

Virtual machine (VM) consolidation in Cloud computing provides a great opportunity for energy saving. However, the obligation of providing suitable quality of service to end users leads to the necessity in dealing with energy-performance tradeoff. In this paper, we propose a redesigned energy-aware heuristic framework for VM consolidation to achieve a better energy-performance tradeoff. There are two main contributions in the framework: (1) establish a service level agreement (SLA) violation decision algorithm to decide whether a host is overload with SLA violation; (2) minimum power and maximum utilization policy is then proposed to improve the Minimum Power policy in previous work. Finally, we have evaluated our framework through simulation on large-scale experiments driven by workload traces from more than a thousand VMs, and the results show that our framework outperforms previous work. Specifically, it guarantees 21---34 % decrease in energy consumption, 84---92 % decrease in SLA violation, 87---94 % decrease in energy-performance metric, and 63 % decrease in execution time. And we further discuss why the redesigned framework outperforms the previous design.
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paper_title: Optimal virtual machine placement across multiple cloud providers

paper_content:

Cloud computing provides users an efficient way to dynamically allocate computing resources to meet demands. Cloud providers can offer users two payment plans, i.e., reservation and on-demand plans for resource provisioning. Price of resources in reservation plan is generally cheaper than that in on-demand plan. However, since the reservation plan has to be acquired in advance, it may not fully meet future demands in which the on-demand plan can be used to guarantee the availability to the user. In this paper, we propose an optimal virtual machine placement (OVMP) algorithm. This algorithm can minimize the cost spending in each plan for hosting virtual machines in a multiple cloud provider environment under future demand and price uncertainty. OVMP algorithm makes a decision based on the optimal solution of stochastic integer programming (SIP) to rent resources from cloud providers. The performance of OVMP algorithm is evaluated by numerical studies and simulation. The results clearly show that the proposed OVMP algorithm can minimize users' budgets. This algorithm can be applied to provision resources in emerging cloud computing environments.
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paper_title: VM Placement Strategies for Cloud Scenarios

paper_content:

The problem of Virtual Machine (VM) placement in a compute cloud infrastructure is well-studied in the literature. However, the majority of the existing works ignore the dynamic nature of the incoming stream of VM deployment requests that continuously arrive to the cloud provider infrastructure. In this paper we provide a practical model of cloud placement management under a stream of requests and present a novel technique called Backward Speculative Placement (BSP)that projects the past demand behavior of a VM to a candidate target host. We exploit the BSP technique in two algorithms, first for handling the stream of deployment requests, second in a periodic optimization, to handle the dynamic aspects of the demands. We show the benefits of our BSPtechnique by comparing the results on a simulation period with a strategy of choosing an optimal placement at each time instant, produced by a generic MIP solver.
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paper_title: Redundant virtual machine placement for fault-tolerant consolidated server clusters

paper_content:

Consolidated server systems using server virtualization involves serious risks of host server failures that induce unexpected downs of all hosted virtual machines and applications. To protect applications requiring high-availability from unpredictable host server failures, redundant configuration using virtual machines can be an effective countermeasure. This paper presents a virtual machine placement method for establishing a redundant configuration against host server failures with less host servers. The proposed method estimates the requisite minimum number of virtual machines according to the performance requirements of application services and decides an optimum virtual machine placement so that minimum configurations survive at any k host server failures. The evaluation results clarify that the proposed method achieves requested fault-tolerance level with less number of hosting servers compared to the conventional N+M redundant configuration approach.
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paper_title: A Matrix Transformation Algorithm for Virtual Machine Placement in Cloud

paper_content:

The virtual machine (VM) placement is an important problem of cloud data center resource management. There have been many research efforts to address this problem. However, existing research is mainly concerned with single objective such as the number of the physical machine (PM) used, and few research consider the resource utilization rate of PM's different dimensions. In this paper, we present a two-objective optimization model such that the number of required PM is minimized and the resource utilization of occupied PM is optimum. We further provide an algorithm based on matrix transformation. In the algorithm, we define cluster matrix of VM request sequence and VM placement initial matrix to generate the optimum solution of VM placement optimization model. Finally, we compare our algorithm with the other VM placement strategy, the experimental results demonstrate the good performance of our algorithm.
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paper_title: Virtual Machine Placement for Improving Energy Efficiency and Network Performance in IaaS Cloud

paper_content:

Under the premise of ensuring application performance, how to place virtual machines (VMs) on physical machines (PMs) to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers in IaaS cloud. The existing VM placement schemes are mostly to reduce energy consumption by optimizing utilization of physical server or network elements, but the issue of aggressive consolidation of VM is ignored, which may lead to network performance degradation. To address the issue, this paper proposes a VM placement scheme based on a new two-stage heuristic algorithm to optimize network performance and to reduce energy consumption of physical servers and network elements, so as to finally achieve the tradeoff between energy efficiency and network performance. The simulations show that our solution achieves good results.
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paper_title: Energy-Saving Virtual Machine Placement in Cloud Data Centers

paper_content:

In cloud data centers, different mapping relationships between virtual machines (VMs) and physical machines (PMs) cause different resource utilization, therefore, how to place VMs on PMs to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers. The existing VM placement schemes are to optimize physical server resources utilization or network resources utilization, but few of them focuses on optimizing multiple resources utilization simultaneously. To address the issue, this paper proposes a VM placement scheme meeting multiple resource constraints, such as the physical server size (CPU, memory, storage, bandwidth, etc.) and network link capacity to improve resource utilization and reduce both the number of active physical servers and network elements so as to finally reduce energy consumption. Since VM placement problem is abstracted as a combination of bin packing problem and quadratic assignment problem, which is also known as a classic combinatorial optimization and NP-hard problem, we design a novel greedy algorithm by combining minimum cut with the best-fit, and the simulations show that our solution achieves better results.
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paper_title: Higher SLA satisfaction in datacenters with continuous VM placement constraints

paper_content:

In a virtualized datacenter, the Service Level Agreement for an application restricts the Virtual Machines (VMs) placement. An algorithm is in charge of maintaining a placement compatible with the stated constraints. Conventionally, when a placement algorithm computes a schedule of actions to re-arrange the VMs, the constraints ignore the intermediate states of the datacenter to only restrict the resulting placement. This situation may lead to temporary violations of the constraints. In this paper, we discuss the causes of these violations. We then advocate for continuous placement constraints to restrict also the actions schedule. We discuss why their development requires more attention and how the extensible placement algorithm BtrPlace can address this issue.
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paper_title: Energy-Efficient Virtual Machine Placement in Data Centers by Genetic Algorithm

paper_content:

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches to the virtual machine placement problem consider the energy consumption by physical machines in a data center only, but do not consider the energy consumption in communication network in the data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement in order to make the data center more energy-efficient. In this paper, we propose a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both the servers and the communication network in the data center. Experimental results show that the genetic algorithm performs well when tackling test problems of different kinds, and scales up well when the problem size increases.
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paper_title: Optimizing data access latencies in cloud systems by intelligent virtual machine placement

paper_content:

Many cloud applications are data intensive requiring the processing of large data sets and the MapReduce/Hadoop architecture has become the de facto processing framework for these applications. Large data sets are stored in data nodes in the cloud which are typically SAN or NAS devices. Cloud applications process these data sets using a large number of application virtual machines (VMs), with the total completion time being an important performance metric. There are many factors that affect the total completion time of the processing task such as the load on the individual servers, the task scheduling mechanism, communication and data access bottlenecks, etc. One dominating factor that affects completion times for data intensive applications is the access latencies from processing nodes to data nodes. Ideally, one would like to keep all data access local to minimize access latency but this is often not possible due to the size of the data sets, capacity constraints in processing nodes which constrain VMs from being placed in their ideal location and so on. When it is not possible to keep all data access local, one would like to optimize the placement of VMs so that the impact of data access latencies on completion times is minimized. We address this problem of optimized VM placement - given the location of the data sets, we need to determine the locations for placing the VMs so as to minimize data access latencies while satisfying system constraints. We present optimal algorithms for determining the VM locations satisfying various constraints and with objectives that capture natural tradeoffs between minimizing latencies and incurring bandwidth costs. We also consider the problem of incorporating inter-VM latency constraints. In this case, the associated location problem is NP-hard with no effective approximation within a factor of 2 - ϵ for any ϵ > 0. We discuss an effective heuristic for this case and evaluate by simulation the impact of the various tradeoffs in the optimization objectives.
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paper_title: A Mathematical Programming Approach for Server Consolidation Problems in Virtualized Data Centers

paper_content:

Today's data centers offer IT services mostly hosted on dedicated physical servers. Server virtualization provides a technical means for server consolidation. Thus, multiple virtual servers can be hosted on a single server. Server consolidation describes the process of combining the workloads of several different servers on a set of target servers. We focus on server consolidation with dozens or hundreds of servers, which can be regularly found in enterprise data centers. Cost saving is among the key drivers for such projects. This paper presents decision models to optimally allocate source servers to physical target servers while considering real-world constraints. Our central model is proven to be an NP-hard problem. Therefore, besides an exact solution method, a heuristic is presented to address large-scale server consolidation projects. In addition, a preprocessing method for server load data is introduced allowing for the consideration of quality-of-service levels. Extensive experiments were conducted based on a large set of server load data from a data center provider focusing on managerial concerns over what types of problems can be solved. Results show that, on average, server savings of 31 percent can be achieved only by taking cycles in the server workload into account.
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paper_title: A virtual machine consolidation framework for MapReduce enabled computing clouds

paper_content:

In nowadays computing clouds, it is of the cloud providers' economic interests to correctly consolidate the workload of the virtual machines (VMs) into the suitable physical servers in the cloud data center in order to minimize the total maintenance cost. However, during the consolidation process, sufficient protection should be provided to the service level agreement (SLA) of the VMs. In this paper, the VM consolidation problem for MapReduce enabled computing clouds has been investigated. In the MapReduce enabled computing clouds, MapReduce jobs are carried out by homogeneous MapReduce VM instances that have identical hardware resource. Two resource allocation schemes with corresponding SLA constraints for the MapReduce VMs and the non-MapReduce VMs are proposed. Based on these schemes, the VM consolidation problem is modeled as an integer nonlinear optimization problem and an efficient algorithm has been proposed to locate its solutions. The results show that better VM consolidation performance can be achieved by colocating MapReduce instances together with non-MapReduce instances in the same set of physical servers.
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paper_title: Reduce Energy Consumption through Virtual Machine Placement in Cloud Data Centre

paper_content:

In this paper, energy consumption in the data centre was studied where thousands of servers and other devices runs, energy are utilized to run the server and cooling the environment. Energy consumption can be reduced by switching off the idle server by means of migration of Virtual Machine from under-load Host. Load in cloud computing is maintained by migration of VM from the overloaded Host to a free Host or activate new Host. Based on this study, a reservation technique by using BIN packing was proposed in this paper with an overload detection algorithm. The proposed algorithm RBIN is experimented in 800 servers with 1024 Virtual Machines. From the experimental result, proposed method RBIN reduces energy in higher level.
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paper_title: Workload characteristics-aware virtual machine consolidation algorithms

paper_content:

An energy conservation strategy must address two issues — placement of virtual machine images and workload characteristics of virtual machines. For performance reason most cloud systems copy a prototype image into the local disk of a physical machine before starting a virtual machine. If the physical machine that stores the image of a virtual machine is off-line, then we cannot run this virtual machine. The workload characteristics of virtual machines determine whether it is data-intensive or CPU-intensive. We assume that the system has a distributed file system therefore a physical machine can run any virtual machine even if it does not have the image. However, we observe that the performance of a data-intensive virtual machine running on a physical machine without its image could result in 60% performance loss compared with running the same virtual machine on a physical machine that has the virtual machine image. On the other hand, the performance of a CPU-intensive virtual machine is almost independent of whether the physical machine has the image or not. As a result, an energy conservation algorithm must consider the workload characteristic of a virtual machine when finding a physical machine to run it, especially for data-intensive virtual machines. This paper proposes a workload characteristics-aware virtual machine consolidation algorithms. We propose an approximation algorithm and two dynamic programmings to consolidate virtual machines and reduce the number of physical machines. We conduct experiments and compare the numbers of physical machines used by our approximation algorithm with the optimal number of physical machines found by our dynamic programming. The experiment results indicate that our approximation algorithm finds good solutions much faster than the dynamic programming.
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paper_title: An energy aware framework for virtual machine placement in cloud federated data centres

paper_content:

Data centres are powerful ICT facilities which constantly evolve in size, complexity, and power consumption. At the same time users' and operators' requirements become more and more complex. However, existing data centre frameworks do not typically take energy consumption into account as a key parameter of the data centre's configuration. To lower the power consumption while fulfilling performance requirements we propose a flexible and energy-aware framework for the (re)allocation of virtual machines in a data centre. The framework, being independent from the data centre management system, computes and enacts the best possible placement of virtual machines based on constraints expressed through service level agreements. The framework's flexibility is achieved by decoupling the expressed constraints from the algorithms using the Constraint Programming (CP) paradigm and programming language, basing ourselves on a cluster management library called Entropy. Finally, the experimental and simulation results demonstrate the effectiveness of this approach in achieving the pursued energy optimization goals.
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paper_title: A Relationship-Based VM Placement Framework of Cloud Environment

paper_content:

Managing computation resources in a cost-effective way has become the core competence for a Cloud provider to win over the market because of the "pay-as-you-go" business model. Therefore, VM placement has become more and more important in the research and practices of VM management by determining at what condition and on which physical server a VM should be placed so that the SLA can be guaranteed and servers' utilization can be improved. Much existing work simply formulates the above issue to be a bin-packing problem, which does not take the VM relationships into account. However, the relationship information can greatly impact the SLA of the Cloud system and the resource utilization. Therefore, in this paper, we propose a relationship-based VM placement framework, SmartCRS, to optimize the VM placement procedure. SmartCRS reveals the relationships between VMs automatically. Then by using such information and based on a constraint library, it gives a proper VM placement plan. Finally, the plan is carried out by SmartCRS automatically or by Cloud administrators manually to improve the server utilization and guarantee the required SLA. Two case studies are conducted to demonstrate the effectiveness and efficiency of the proposed framework at the end of this paper.
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paper_title: EQVMP: Energy-efficient and QoS-aware virtual machine placement for software defined datacenter networks

paper_content:

To provide effective and reliable services, cloud datacenters need parallel computing and virtualization techniques. This work presents an improved virtual machine (VM) placement mechanism, called Energy efficiency and Quality of Service (QoS) aware VM Placement (EQVMP) to overcome the problem of unbalanced traffic load in switching on and off VMs for the purpose of energy saving. EQVMP combines three key techniques: (1) hop reduction, (2) energy saving and (3) load balancing. Hop reduction can regroup VMs to lower the traffic load among them. Energy saving techniques aim at choosing the appropriate servers. The proposed load balancing updates VM placement periodically. Our experimental results show that the proposed scheme can lower energy consumption and maintain QoS. We propose an evaluation score [1] to assess VM placement in terms of energy, delay and throughput. Comparing to other existing placement policies, our proposed mechanism can enhance system throughput by 25% and can have better evaluation score.
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paper_title: Optimal update frequency model for physical machine state change and virtual machine placement in the cloud

paper_content:

Cloud computing is evolving into the default operational framework running modern data centers. Efficient data center operation is concerned with the total amount of energy consumed as well as assuring adequate resources are available to process all of the incoming work requests. Existing research has demonstrated several algorithms that can be used to determine the optimal number of resources required to service these requests. However, a key issue not addressed in these algorithms is determining the frequency of recalculating the number of required resources. Changing the required resources at a rate slower than the optimal update frequency results in lower energy efficiency due to the over allocation of resources. Changing the resources at a rate higher than the optimal frequency results in insufficient time for systems to change state, which results in SLA violations. In this paper, a stochastic optimization model is presented that determines the optimal update frequency for changing the states of the nodes of the cloud as well as determining the proper frequency for recalculating the maximum expected load, which improves the determination of the optimum number of resources required, therefore maximizes energy efficiency and minimizes SLA violations.
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paper_title: Network-aware virtual machine consolidation for large data centers

paper_content:

Resource management in modern data centers has become a challenging task due to the tremendous growth of data centers. In large virtual data centers, performance of applications is highly dependent on the communication bandwidth available among virtual machines. Traditional algorithms either do not consider network I/O details of the applications or are computationally intensive. We address the problem of identifying the virtual machine clusters based on the network traffic and placing them intelligently in order to improve the application performance and optimize the network usage in large data center. We propose a greedy consolidation algorithm that ensures the number of migrations is small and the placement decisions are fast, which makes it practical for large data centers. We evaluated our approach on real world traces from private and academic data centers, using simulation and compared the existing algorithms on various parameters like scheduling time, performance improvement and number of migrations. We observed a ~70% savings of the interconnect bandwidth and overall ~60% improvements in the applications performances. Also, these improvements were produced within a fraction of scheduling time and number of migrations.
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paper_title: Energy Efficient VM Placement Supported by Data Analytic Service

paper_content:

The popularity and commercial use of cloud computing has prompted an increased concern among cloud service providers for energy efficiency while still maintaining quality of service. One of the key techniques used for the efficient use of cloud server resources is virtual machine placement. This work introduces a precise VM placement algorithm that ensures energy efficiency and also prevents Service Level Agreement (SLA) violation. The mathematical model of the algorithm is supported by a sophisticated data analytic system implemented as a service. The precision of the algorithm is achieved by allowing each individual VM to build its own data model on demand over an appropriate time horizon. Thus the data model can reflect accurately the characteristics of resource usage of the VM. The algorithm can communicate synchronously or asynchronously with the data analytic service which is deployed as a cloud-based solution. In the experiments, several advanced data modelling and use forecasting techniques were evaluated. Results from simulation-based experiments show that the VM placement algorithm (supported by the data analytic service) can effectively reduce power consumption, the number of VM migrations, and prevent SLA violation, it also compares very favourably with other placement algorithms.
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paper_title: Evolutionary Optimal Virtual Machine Placement and Demand Forecaster for Cloud Computing

paper_content:

Cloud computing allows the users to efficiently and dynamically provision computing resources to meet their IT needs. Most cloud providers offer two types of payment plans to the user, i.e., reservation and on-demand. The reservation plan is typically cheaper than the on-demand plan but reservation plan has to be provisioned in advance. Reserving the resources would be straightforward if the actual computing demand (e.g., job processing) is known in advance. However, in reality, the actual computing demand can be observed only at the point of actual usage. Therefore, it is difficult to reserve the correct amount of resources during the reservation to meet the computing demands of the users. In this paper, we propose an evolutionary optimal virtual machine placement (EOVMP) algorithm with a demand forecaster. First, a demand forecaster predicts the computing demand. Then, EOVMP uses this predicted demand to allocate the virtual machines using reservation and on-demand plans for job processing. The performance of the proposed schemes is evaluated by simulations and numerical studies. The evaluation result shows that the EOVMP algorithm can provide the solution close to the optimal solution of stochastic integer programming (SIP) and the prediction of the demand forecaster is of reasonable accuracy.
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paper_title: Maximum migration time guarantees in dynamic server consolidation for virtualized data centers

paper_content:

Server consolidation is a vital mechanism in modern data centers in order to minimize expenses with infrastructure. In most cases, server consolidation may require migrating virtual machines between different physical servers. Although the downtime of live-migration is negligible, the amount of time to migrate all virtual machines can be substantial, delaying the completion of the consolidation process. This paper proposes a new server consolidation algorithm, which guarantees that migrations are completed in a given maximum time. The migration time is estimated using the max-min fairness model, in order to consider the competition of migration flows for the network infrastructure. The algorithm was simulated using a real workload and shows a good consolidation ratio in comparison to other algorithms, while also guaranteeing a maximum migration time.
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paper_title: A Stable Network-Aware VM Placement for Cloud Systems

paper_content:

Virtual Machine (VM) placement has to carefully consider the aggregated resource consumption of co-located VMs in order to obey service level agreements at lower possible cost. In this paper, we focus on satisfying the traffic demands of the VMs in addition to CPU and memory requirements. This is a much more complex problem both due to its quadratic nature (being the communication between a pair of VMs) and since it involves many factors beyond the physical host, like the network topologies and the routing scheme. Moreover, traffic patterns may vary over time and predicting the resulting effect on the actual available bandwidth between hosts within the data center is extremely difficult. We address this problem by trying to allocate a placement that not only satisfies the predicted communication demand but is also resilient to demand time-variations. This gives rise to a new optimization problem that we call the Min Cut Ratio-aware VM Placement (MCRVMP). The general MCRVMP problem is NP-Hard, hence, we introduce several heuristics to solve it in reasonable time. We present extensive experimental results, associated with both placement computation and run-time performance under time-varying traffic demands, to show that our heuristics provide good results (compared to the optimal solution) for medium size data centers.
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paper_title: PBFVMC: A New Pseudo-Boolean Formulation to Virtual-Machine Consolidation

paper_content:

Over the course of the last decade, there have been several improvements in the performance of Boolean Satisfiability (SAT), Integer Linear Programming (ILP) and Pseudo-Boolean Optimization (PBO) solvers. These improvements have encouraged the applications of SAT, ILP and PBO techniques in modeling complex engineering problems. One such problem is the Virtual Machine Consolidation. The Virtual Machine Consolidation problem consists in placing a set of virtual machines in a set of hardware in a way to increase workload on hardware where they can operate more energy-efficient. This paper proposes an improved PBO formulation of the Virtual Machine Consolidation problem, PBFVMC. The improved formulation and enhancements are built on top of a previous work and a new set of constraints is created and rationalized to work more friendly with current PBO solvers. It is observed that this new formulation goes a step ahead and more problems can now be solved.
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paper_title: Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center

paper_content:

Powerful data centers are the essential supporting infrastructure for mobile, ubiquitous, and cognitive computing, which are the most popular computing paradigms to utilize all kinds of physical resources and provide various services. To ensure the high quality of services, the performance and cost of a data center is a critical factor. In this paper, we investigate the issue of increasing the resource utilization of data centers to improve their performance and lower the cost. It is an efficient way to increase resource utilization via resource sharing. Technically, server virtualization provides the opportunity to share resources in data centers. However, it also introduces other problems, the primary problem being virtual machine placement (VMP), which is to choose a proper physical machine (PM) to deploy virtual machines (VMs) in runtime. We study the virtual machine placement problem with the target of minimizing the total energy consumption by the running of PMs, which is also an indication of resource utilization and the cost of a data center. Due to the multiple dimensionality of physical resources, there always exists a waste of resources, which results from the imbalanced use of multi-dimensional resources. To characterize the multi-dimensional resource usage states of PMs, we present a multi-dimensional space partition model. Based on this model, we then propose a virtual machine placement algorithm EAGLE, which can balance the utilization of multi-dimensional resources, reduce the number of running PMs, and thus lower the energy consumption. We also evaluate our proposed balanced algorithm EAGLE via extensive simulations and experiments on real traces. Experimental results show, over the long run, that EAGLE can save as much as 15% more energy than the first fit algorithm.
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paper_title: A Network-aware Virtual Machine Placement and Migration Approach in Cloud Computing

paper_content:

Cloud computing represents a major step up in computing whereby shared computation resources are provided on demand. In such a scenario, applications and data thereof can be hosted by various networked virtual machines (VMs). As applications, especially data-intensive applications, often need to communicate with data frequently, the network I/O performance would affect the overall application performance significantly. Therefore, placement of virtual machines which host an application and migration of these virtual machines while the unexpected network latency or congestion occurs is critical to achieve and maintain the application performance. To address these issues, this paper proposes a virtual machine placement and migration approach to minimizing the data transfer time consumption. Our simulation studies suggest that the proposed approach is effective in optimizing the data transfer between the virtual machine and data, thus helping optimize the overall application performance.
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paper_title: Power-Efficient Virtual Machine Placement and Migration in Data Centers

paper_content:

In this paper, we propose a power-efficient solution for virtual machine placement and migration in a fat tree data center network. This solution reduces power consumption as well as job delay by aggregating virtual machines to a few hyper visors and migrating communicating parties to close locations. In this work, we consider OpenFlow as the implementation protocol. In an OpenFlow environment, a centralized controller oversees job loads, virtual machine requirements and hardware availability. Given observation of such global knowledge, the OpenFlow controller can schedule jobs and distribute virtual machines accordingly. As jobs change and flows shift, the OpenFlow controller dynamically adjusts virtual machine assignments by aggregating virtual machines to close locations in order to save energy. With this placement and migration proposal, more jobs can operate concurrently with close sources and destinations of flows, thus both job and flow delay can be reduced.
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paper_title: Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments

paper_content:

Server consolidation using virtualization technology has become increasingly important for improving data center efficiency. It enables one physical server to host multiple independent virtual machines (VMs), and the transparent movement of workloads from one server to another. Fine-grained virtual machine resource allocation and reallocation are possible in order to meet the performance targets of applications running on virtual machines. On the other hand, these capabilities create demands on system management, especially for large-scale data centers. In this paper, a two-level control system is proposed to manage the mappings of workloads to VMs and VMs to physical resources. The focus is on the VM placement problem which is posed as a multi-objective optimization problem of simultaneously minimizing total resource wastage, power consumption and thermal dissipation costs. An improved genetic algorithm with fuzzy multi-objective evaluation is proposed for efficiently searching the large solution space and conveniently combining possibly conflicting objectives. The simulation-based evaluation using power-consumption and thermal-dissipation models based on profiling of a Blade Center, demonstrates the good performance, scalability and robustness of our proposed approach. Compared with four well-known bin-packing algorithms and two single-objective approaches, the solutions obtained from our approach seek good balance among the conflicting objectives while others cannot.
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paper_title: Dynamic Energy-Efficient Virtual Machine Placement Optimization for Virtualized Clouds

paper_content:

A virtual machine placement strategy based on the trade-off between energy consumption and SLA is presented. Aiming at dynamical changes of workload requirements, a self-adaptive placement strategy RLWR based on robust local weight regression is presented, which could decide the overload time of hosts dynamically. After detecting overloaded hosts, one virtual machine migration selection algorithm MNM is proposed. The MNM’s objective is to get minimal migration number. The migrated virtual machines are deployed using bin-packing algorithm PBFDH. The experimental results show that our algorithm has obvious advantages than other algorithms.
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paper_title: Server consolidation with migration control for virtualized data centers

paper_content:

Virtualization has become a key technology for simplifying service management and reducing energy costs in data centers. One of the challenges faced by data centers is to decide when, how, and which virtual machines (VMs) have to be consolidated into a single physical server. Server consolidation involves VM migration, which has a direct impact on service response time. Most of the existing solutions for server consolidation rely on eager migrations, which try to minimize the number of physical servers running VMs. These solutions generate unnecessary migrations due to unpredictable workloads that require VM resizing. This paper proposes an LP formulation and heuristics to control VM migration, which prioritize virtual machines with steady capacity. We performed experiments using TU-Berlin and Google data center workloads to compare our migration control strategy against existing eager-migration-based solutions. We observed that avoiding migration of VMs with steady capacity reduces the number of migrations with minimal penalty in the number of physical servers.
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paper_title: Inter-and-intra data center VM-placement for energy-efficient large-Scale cloud systems

paper_content:

Data centers play the crucial role in the delivery of Cloud services by enabling on-demand access to the shared resources such as software, platform, and infrastructure. Virtual Machine (VM) allocation is one of the challenging tasks in the data center management since user requirements, typically expressed as SLAs, have to be met with the minimum operational expenditure. Despite their huge processing and storage facilities, data centers are among the major contributors of the GreenHouse Gas (GHG) emissions of the IT services. In this paper, we propose a holistic approach for a large-scale Cloud system where the Cloud services are provisioned by several data centers interconnected over the backbone network. We propose a Mixed Integer Linear Programming (MILP) formulation that aims at virtualizing the backbone topology and placing the VMs in data centers with the objective of minimum power consumption. We compare our proposed solution with a benchmark MILP model which selects the closest data centers that can accommodate the user requests taking into account the CPU, memory, and bandwidth capacities of the residing physical hosts. Collected experimental results show the benefit of the proposed management scheme in terms of power consumption, resource utilization, and fairness for medium size data centers.
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paper_title: Provisioning of requests for virtual machine sets with placement constraints in IaaS clouds

paper_content:

We study the problem of optimising the provisioning of collections of virtual machines (VMs) having different placement constraints (e.g., security and anti-collocation) and characteristics (e.g., memory and disk capacity), given a set of physical machines (PMs) with known specifications, in order to achieve the objective of maximising an IaaS cloud provider's revenue. We propose two approaches. The first is based on the formulation of the problem as an integer linear programming (ILP) problem, the solution to which provides an optimal VM placement. The second approach is a heuristic based on classifying the requests into different categories and satisfying the constraints in a particular order using a first lit decreasing (FFD) algorithm for multi-dimensional vector bin packing problem. Given a model of VM placement constraints, offered resources and requests with multiple VM types, both approaches devise a placement plan in a way that maximizes revenue, having due regard both to customer requirements and PM capacities. We evaluate the relative performance of the solutions by means of numerical experiments. The results suggest the optimal solution is not practical for medium to large problems, but it is encouraging that the placement plans of the heuristic are close to those of the optimal solution for smaller problem sizes. We use the heuristic to generate results for large scale placement problems; experiments suggest that it is practical in terms of its runtime efficiency and can provide an effective means of online VM-to-PM mapping.
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paper_title: Efficient VM placement with multiple deterministic and stochastic resources in data centers

paper_content:

Virtual machines (VMs) may significantly improve the efficiency of data center infrastructure by sharing resources of physical servers. This benefit relies on an efficient VM placement scheme to minimize the number of required servers. Existing VM placement algorithms usually assume that VMs' demands for resources are deterministic and stable. However, for certain resources, such as network bandwidth, VMs' demands are bursty and time varying, and demonstrate stochastic nature. In this paper, we study efficient VM placement in data centers with multiple deterministic and stochastic resources. First, we formulate the Multidimensional Stochastic VM Placement (MSVP) problem, with the objective to minimize the number of required servers and at the same time satisfy a predefined resource availability guarantee. Then, we show that the problem is NP-hard, and propose a polynomial time algorithm called Max-Min Multidimensional Stochastic Bin Packing (M3SBP). The basic idea is to maximize the minimum utilization ratio of all the resources of a server, while satisfying the demands of VMs for both deterministic and stochastic resources. Next, we conduct simulations to evaluate the performance of M3SBP. The results demonstrate that M3SBP guarantees the availability requirement for stochastic resources, and M3SBP needs the smallest number of servers to provide the guarantee among the benchmark algorithms.
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paper_title: Data-Aware Virtual Machine Placement and Rate Allocation in Cloud Environment

paper_content:

Infrastructure-as-a-service cloud provides a suitable environment where cloud users can run data-intensive applications and store their required data files. The performance of data-intensive applications strongly depends on data transmission delay. This delay is a function of the size of data files, the location of virtual machines that run the applications as well as the allocation of data rates to virtual machines. In this paper, we propose a novel virtual machine placement algorithm that jointly optimizes the placement of virtual machines as well as their allocated data rates. Through Simulation results we show that the proposed algorithm can significantly reduce the data transfer delay for virtual machines compared to placement algorithms previously proposed in the literature.
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paper_title: Energy efficient VM placement heuristic algorithms comparison for cloud with multidimensional resources

paper_content:

Cloud computing provides user utility-oriented IT services, yet accompanied with huge energy consuming, which contributes to the high operational cost as well as CO2 emission. Making Cloud computing energy efficient can lead to a better tradeoff between profit and environmental impact. In this paper, we formulate the energy efficient VM placement problem in Cloud architecture with multidimensional resources and introduce the objective of this problem. Heuristic algorithms including three traditional local search algorithms and generic algorithm (GA) are presented to provide possible optimized solution. We conduct experiments based on Cloudsim. The result shows that GA sometimes provide the best solution, but with poor stabability. Although the BF provide neither the best nor the worst solution most of time, it have the best stabability.
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paper_title: Energy-Efficient Virtual Machine Replication and Placement in a Cloud Computing System

paper_content:

By utilizing Virtual Machines (VM) and doing server consolidation in a datacenter, a cloud provider can reduce the total energy consumption for servicing his clients with little performance degradation. In particular, the cloud provider can take advantage of dissimilar workloads and by assigning these workloads to the same server, can utilize fewer active servers to service his clients. Placing multiple copies of a VM on different servers and distributing the incoming requests among these VM copies can reduce the resource requirement for each VM copy and help the cloud provider utilize the servers more efficiently. In this paper, the problem of energy-efficient VM placement in a cloud computing system is solved. Precisely, we present an approach that first creates multiple copies of VMs and then uses dynamic programming and local search to place these copies on the physical servers. Simulation results show that the proposed algorithm reduces the total energy consumption by up to 20% with respect to previous work.
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paper_title: Dynamic optimization of virtual machine placement by resource usage prediction

paper_content:

In order to save energy with preventing the lack of the resources on servers in data centers, we address the dynamic optimization of virtual machine placement. A decision problem of the virtual machine placement has been formulated as Bin Packing problem. Conventional methods repeat to solve the problem at regular intervals. Live migration is used for changing the virtual machine placement, but some of live migrations are unnecessary; the virtual machine is migrated repeatedly between the physical servers. For the purpose of reducing the unnecessary live migrations, we propose a dynamic optimization of virtual machine placement by resource usage prediction. The proposed method predicts the future resource usage by Auto Regressive Model. The proposed method decides the virtual machine placement by solving the Bin Packing problem with the predicted resource usage.
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paper_title: An Optimization-Based Scheme for Efficient Virtual Machine Placement

paper_content:

According to the important methodology of convex optimization theory, the energy-efficient and scalability problems of modern data centers are studied. Then a novel virtual machine (VM) placement scheme is proposed for solving these problems in large scale. Firstly, by referring the definition of VM placement fairness and utility function, the basic algorithm of VM placement which fulfills server constraints of physical machines is discussed. Then, we abstract the VM placement as an optimization problem which considers the inherent dependencies and traffic between VMs. By given the structural differences of recently proposed data center architectures, we further investigate a comparative analysis on the impact of the network architectures, server constraints and application dependencies on the potential performance gain of optimization-based VM placement. Comparing with the existing schemes, the performance improvements are illustrated from multiple perspectives, such as reducing the number of physical machines deployment, decreasing communication cost between VMs, improving energy-efficient and scalability of data centers.
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paper_title: Server Consolidation Algorithms with Bounded Migration Cost and Performance Guarantees in Cloud Computing

paper_content:

Consolidation of virtual machines is essential to achieve energy optimization in cloud computing environments. As virtual machines dynamically enter and leave a cloud system, it becomes necessary to relocate virtual machines among servers. However, relocation of virtual machines introduces run-time overheads and consumes extra energy, thus an careful planning for relocation is necessary. We model the relocation problem as a modified bin packing problem and propose a new server consolidation algorithm that guarantees server consolidation with {\em bounded} relocation costs. We also conduct a detailed analysis on the complexity of the server consolidation problem, and give a upper bound on the cost of relocation. Finally, we conduct simulations and compare our server consolidation algorithm with other relocation methods, like First Fit and Best Fit method. The experiment results suggest an interesting trade-off between server consolidation quality and relocation cost. Our algorithm is able to trade about 1\% in server consolidation quality for a reduction about 50\% in relocation cost, when compared with other well known bin packing algorithms. We also note that the relocation cost incurred in our method is much less than the theoretical bound we provided. The reason is that we overestimate the amount of relocation from theoretical analysis, and the actual amount of relocation found from experiments is much less than the worst-case bound from theoretical analysis.
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paper_title: Guaranteeing High Availability Goals for Virtual Machine Placement

paper_content:

The placement of virtual machines (VMs) on a cluster of hosts under multiple constraints, including administrative (security, regulations) resource-oriented (capacity, energy), and QoS-oriented (performance) is a highly complex task. We define a new high-availability property for a VM, when a VM is marked as k-resilient, as long as there are up to k host failures, it should be guaranteed that it can be relocated to a non-failed host without relocating other VMs. Together with Hardware Predictive Failure Analysis and live migration, which enable VMs to be evacuated from a host before it fails, this property allows the continuous running of VMs on the cluster despite host failures. The complexity of the constraints associated with k-resiliency, which are naturally expressed by Second Order logic statements, prevented their integration into the placement computation until now. We present a novel algorithm which enables this integration by transforming the k-resiliency constraints to rules consumable by a generic Constraint Programming engine, prove that it guarantees the required resiliency and describe the implementation. We provide some preliminary results and compare our high availability support with naive solutions.
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paper_title: Migration-based virtual machine placement in cloud systems

paper_content:

Cloud computing is an emerging technology that greatly shapes our lives, where users run jobs on virtual machines (VMs) on physical machines (PMs) provided by a cloud provider, saving the investment in upfront infrastructures. Due to the heterogeneity of various jobs, different VMs on the same PM can have different job completion times. Meanwhile, the PMs are also heterogeneous. Therefore, different VM placements have different job completion times. Our objective is to minimize the total job completion time of the input VM requests through a reasonable VM placement schedule. This problem is NP-hard, since it can be reduced to a knapsack problem. We propose an off-line VM placement method through emulated VM migration, while the on-line VM placement is solved by a real VM migration process. The migration algorithm is a heuristic approach, where we place the VM to its best PM directly, as long as it has enough capacity. Otherwise, if the migration constraint is satisfied, we migrate another VM from this PM to accommodate the new VM. Furthermore, we study a hybrid scheme where a batch is employed to accept upcoming VMs for the on-line scenario. Evaluation results prove the high efficiency of our algorithms.
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paper_title: On Theory of VM Placement: Anomalies in Existing Methodologies and Their Mitigation Using a Novel Vector Based Approach

paper_content:

In this paper, we present the methodologies used in existing literature for Virtual Machine (VM) placement, load balancing and server consolidation in a data center environment. While the methodologies may seem fine on the surface, certain drawbacks and anomalies can be uncovered when they are analyzed deeper. We point out those anomalies and draw backs in the existing literature and explain what are the root causes of such anomalies. Then we propose a novel methodology based on vector arithmetic which not only addresses those anomalies but also leads to some interesting theories and algorithms to tackle the above mentioned three functionalities required in managing resources of data centers. We believe that with a strong mathematical base, our methodology has the potential to become the foundation of future models and algorithms in this research area.
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paper_title: Multiobjective Virtual Machine Placement in Cloud Environment

paper_content:

In this paper, the virtual machine placement problem is formulated as a multi-objective optimization problem. The objectives are maximizing profit, maximizing load balancing and minimizing recourse wastage. Results of Genetic algorithms, Non-dominated Sorting Genetic Algorithm and Non-dominated Sorting Genetic Algorithm-II are compared with common solution representation, penalty and benefit values. All the three algorithms reported good solutions whereas GA and NSGA are subjected to premature convergence and duplicate solutions. NSGA-II gives good and diversified range of solutions.
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paper_title: Virtual machine placement for predictable and time-constrained peak loads

paper_content:

We present an approach to optimal virtual machine placement within datacenters for predicable and time-constrained load peaks. A method for optimal load balancing is developed, based on binary integer programming. For tradeoffs between quality of solution and computation time, we also introduce methods to pre-process the optimization problem before solving it. Upper bound based optimizations are used to reduce the time required to compute a final solution, enabling larger problems to be solved. For further scalability, we also present three approximation algorithms, based on heuristics and/or greedy formulations. The proposed algorithms are evaluated through simulations based on synthetic data sets. The evaluation suggests that our algorithms are feasible, and that these can be combined to achieve desired tradeoffs between quality of solution and execution time.
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paper_title: On Modelling Virtual Machine Consolidation to Pseudo-Boolean Constraints

paper_content:

Cloud Computing is a new paradigm of distributed computing that offers virtualized resources and services over the Internet. To offer Infrastructure-as-a-Service (IaaS) many Cloud providers uses a large data center which usage ranges 5% to 10% of capacity in average. In order to improve Cloud data center management and resources usage a Virtual Machine (VM) consolidation technique can be applied to increase workloads and save energy. Using VM consolidation, we introduce an artificial intelligence consolidation based in Pseudo-Boolean (PB) Constraints to find a optimal consolidation. To evaluate our PB consolidation approach we used the DInf-UFPR and Google Cluster scenario and the formulas are solved with two state-of-the-art solvers.
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paper_title: Virtual Machine Placement Optimization Supporting Performance SLAs

paper_content:

Cloud computing model separates usage from ownership in terms of control on resource provisioning. Resources in the cloud are projected as a service and are realized using various service models like IaaS, PaaS and SaaS. In IaaS model, end users get to use a VM whose capacity they can specify but not the placement on a specific host or with which other VMs it can be co-hosted. Typically, the placement decisions happen based on the goals like minimizing the number of physical hosts to support a given set of VMs by satisfying each VMs capacity requirement. However, the role of the VMM usage to support I/O specific workloads inside a VM can make this capacity requirement incomplete. I/O workloads inside VMs require substantial VMM CPU cycles to support their performance. As a result, placement algorithms need to include the VMM's usage on a per VM basis. Secondly, cloud centers encounter situations wherein change in existing VM's capacity or launching of new VMs need to be considered during different placement intervals. Usually, this change is handled by migrating existing VMs to meet the goal of optimal placement. We argue that VM migration is not a trivial task and does include loss of performance during migration. We quantify this migration overhead based on the VM's workload type and include the same in placement problem. One of the goals of the placement algorithm is to reduce the VM's migration prospects, thereby reducing chances of performance loss during migration. This paper evaluates the existing ILP and First Fit Decreasing (FFD) algorithms to consider these constraints to arrive at placement decisions. We observe that ILP algorithm yields optimal results but needs long computing time even with parallel version. However, FFD heuristics are much faster and scalable algorithms that generate a sub-optimal solution, as compared to ILP, but in time-scales that are useful in real-time decision making. We also observe that including VM migration overheads in the placement algorithm results in a marginal increase in the number of physical hosts but a significant, of about 84 percent reduction in VM migration.
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paper_title: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing

paper_content:

Virtual machine placement is a process of mapping virtual machines to physical machines. The optimal placement is important for improving power efficiency and resource utilization in a cloud computing environment. In this paper, we propose a multi-objective ant colony system algorithm for the virtual machine placement problem. The goal is to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption. The proposed algorithm is tested with some instances from the literature. Its solution performance is compared to that of an existing multi-objective genetic algorithm and two single-objective algorithms, a well-known bin-packing algorithm and a max-min ant system (MMAS) algorithm. The results show that the proposed algorithm is more efficient and effective than the methods we compared it to.
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paper_title: Design and Implementation of a Multi-objective Optimization Mechanism for Virtual Machine Placement in Cloud Computing Data Center

paper_content:

Cloud computing is becoming a popular way of supplying and using computing resources. A cloud-computing data center is equipped with a large number of physical resources and must manage an even larger number of virtual machines (VMs). The center’s VM placement strategy affects the utilization of physical resources, and consequently, it influences operational costs. Our goal is to develop a multi-objective optimization mechanism for VM placement that satisfies various constraints and results in the lowest operational cost. The number of possible combinations of VMs and hosts can be extremely large. For the mechanism to be practical, the number of possible combinations must be reduced. We reduced computational overheads by classifying VM hosts into a relatively small number of equivalent sets. Simulation results show that expected operational costs can be significantly reduced by applying the proposed mechanism.
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paper_title: An Availability-Aware Virtual Machine Placement Approach for Dynamic Scaling of Cloud Applications

paper_content:

Cloud computing promises customers the on-demand ability to dynamically provision virtualization resources in face of workload variations. Most existing scaling approaches addressed this problem by allocating application to a certain amount of cloud resources. However, the problem of the availability of application influenced by VM-based physical locations during resource scaling process is a serious challenge due to dynamic complex workload and has not been widely discussed yet. In this paper, we present a novel availability-based computing model to describe availability attribute of one application in the hierarchical structured cloud. Moreover, we propose an availability-aware policy by performing both vertical and horizontal scaling to explore how and where to allocate computing resource. Simulation results indicate that our model captured the availability of cloud applications properly and proposed scaling approach achieves the objectives of meeting availability demands and minimizing the total communication cost.
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paper_title: An energy-aware heuristic framework for virtual machine consolidation in Cloud computing

paper_content:

Virtual machine (VM) consolidation in Cloud computing provides a great opportunity for energy saving. However, the obligation of providing suitable quality of service to end users leads to the necessity in dealing with energy-performance tradeoff. In this paper, we propose a redesigned energy-aware heuristic framework for VM consolidation to achieve a better energy-performance tradeoff. There are two main contributions in the framework: (1) establish a service level agreement (SLA) violation decision algorithm to decide whether a host is overload with SLA violation; (2) minimum power and maximum utilization policy is then proposed to improve the Minimum Power policy in previous work. Finally, we have evaluated our framework through simulation on large-scale experiments driven by workload traces from more than a thousand VMs, and the results show that our framework outperforms previous work. Specifically, it guarantees 21---34 % decrease in energy consumption, 84---92 % decrease in SLA violation, 87---94 % decrease in energy-performance metric, and 63 % decrease in execution time. And we further discuss why the redesigned framework outperforms the previous design.
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paper_title: Optimal virtual machine placement across multiple cloud providers

paper_content:

Cloud computing provides users an efficient way to dynamically allocate computing resources to meet demands. Cloud providers can offer users two payment plans, i.e., reservation and on-demand plans for resource provisioning. Price of resources in reservation plan is generally cheaper than that in on-demand plan. However, since the reservation plan has to be acquired in advance, it may not fully meet future demands in which the on-demand plan can be used to guarantee the availability to the user. In this paper, we propose an optimal virtual machine placement (OVMP) algorithm. This algorithm can minimize the cost spending in each plan for hosting virtual machines in a multiple cloud provider environment under future demand and price uncertainty. OVMP algorithm makes a decision based on the optimal solution of stochastic integer programming (SIP) to rent resources from cloud providers. The performance of OVMP algorithm is evaluated by numerical studies and simulation. The results clearly show that the proposed OVMP algorithm can minimize users' budgets. This algorithm can be applied to provision resources in emerging cloud computing environments.
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paper_title: VM Placement Strategies for Cloud Scenarios

paper_content:

The problem of Virtual Machine (VM) placement in a compute cloud infrastructure is well-studied in the literature. However, the majority of the existing works ignore the dynamic nature of the incoming stream of VM deployment requests that continuously arrive to the cloud provider infrastructure. In this paper we provide a practical model of cloud placement management under a stream of requests and present a novel technique called Backward Speculative Placement (BSP)that projects the past demand behavior of a VM to a candidate target host. We exploit the BSP technique in two algorithms, first for handling the stream of deployment requests, second in a periodic optimization, to handle the dynamic aspects of the demands. We show the benefits of our BSPtechnique by comparing the results on a simulation period with a strategy of choosing an optimal placement at each time instant, produced by a generic MIP solver.
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paper_title: Elasticity-aware virtual machine placement for cloud datacenters

paper_content:

With the increasing popularity of cloud computing, the cloud datacenter suffers from both limited resources and the variation of users' requests. One important feature of cloud computing is on-demand scaling, enabling the fluctuation of one user's resource demand. However, amongst previous work concerning the virtual machine (VM) placement in datacenters, satisfying the VMs' requested resources of users is the primary objective, neglecting future demand variation. In this paper, we propose the concept of elasticity, referring to how well the datacenter can satisfy the growth of the input VMs resource demands under both the limitations of physical machines (PMs) capacities and links capacities. To consider both dimensions of the machine and bandwidth resources simultaneously, we propose our hierarchical VM placement algorithm. We also prove the optimality of our algorithm in a frequently used semi-homogeneous datacenter configuration. Furthermore, we study the heterogeneous datacenter configuration, favoring the characteristics of multi-tenant datacenters. Evaluation results validate the efficiency of our algorithm.
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paper_title: A Matrix Transformation Algorithm for Virtual Machine Placement in Cloud

paper_content:

The virtual machine (VM) placement is an important problem of cloud data center resource management. There have been many research efforts to address this problem. However, existing research is mainly concerned with single objective such as the number of the physical machine (PM) used, and few research consider the resource utilization rate of PM's different dimensions. In this paper, we present a two-objective optimization model such that the number of required PM is minimized and the resource utilization of occupied PM is optimum. We further provide an algorithm based on matrix transformation. In the algorithm, we define cluster matrix of VM request sequence and VM placement initial matrix to generate the optimum solution of VM placement optimization model. Finally, we compare our algorithm with the other VM placement strategy, the experimental results demonstrate the good performance of our algorithm.
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paper_title: Higher SLA satisfaction in datacenters with continuous VM placement constraints

paper_content:

In a virtualized datacenter, the Service Level Agreement for an application restricts the Virtual Machines (VMs) placement. An algorithm is in charge of maintaining a placement compatible with the stated constraints. Conventionally, when a placement algorithm computes a schedule of actions to re-arrange the VMs, the constraints ignore the intermediate states of the datacenter to only restrict the resulting placement. This situation may lead to temporary violations of the constraints. In this paper, we discuss the causes of these violations. We then advocate for continuous placement constraints to restrict also the actions schedule. We discuss why their development requires more attention and how the extensible placement algorithm BtrPlace can address this issue.
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paper_title: Shadow-routing based dynamic algorithms for virtual machine placement in a network cloud

paper_content:

We consider a shadow routing based approach to the problem of real-time adaptive placement of virtual machines (VM) in large data centers (DC) within a network cloud. Such placement in particular has to respect vector packing constraints on the allocation of VMs to host physical machines (PM) within a DC, because each PM can potentially serve multiple VMs simultaneously. Shadow routing is attractive in that it allows a large variety of system objectives and/or constraints to be treated within a common framework (as long as the underlying optimization problem is convex). Perhaps even more attractive feature is that the corresponding algorithm is very simple to implement, it runs continuously, and adapts automatically to changes in the VM demand rates, changes in system parameters, etc., without the need to re-solve the underlying optimization problem “from scratch”. In this paper we focus on the minmax-DC-load problem. Namely, we propose a combined VM-toDC routing and VM-to-PM assignment algorithm, referred to as Shadow scheme, which minimizes the maximum of appropriately defined DC utilizations. We prove that the Shadow scheme is asymptotically optimal (as one of its parameters goes to 0). Simulation confirms good performance and high adaptivity of the algorithm. Favorable performance is also demonstrated in comparison with a baseline algorithm based on VMware implementation [7], [8]. We also propose a simplified - “more distributed” - version of the Shadow scheme, which performs almost as well in simulations.
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paper_title: Optimizing data access latencies in cloud systems by intelligent virtual machine placement

paper_content:

Many cloud applications are data intensive requiring the processing of large data sets and the MapReduce/Hadoop architecture has become the de facto processing framework for these applications. Large data sets are stored in data nodes in the cloud which are typically SAN or NAS devices. Cloud applications process these data sets using a large number of application virtual machines (VMs), with the total completion time being an important performance metric. There are many factors that affect the total completion time of the processing task such as the load on the individual servers, the task scheduling mechanism, communication and data access bottlenecks, etc. One dominating factor that affects completion times for data intensive applications is the access latencies from processing nodes to data nodes. Ideally, one would like to keep all data access local to minimize access latency but this is often not possible due to the size of the data sets, capacity constraints in processing nodes which constrain VMs from being placed in their ideal location and so on. When it is not possible to keep all data access local, one would like to optimize the placement of VMs so that the impact of data access latencies on completion times is minimized. We address this problem of optimized VM placement - given the location of the data sets, we need to determine the locations for placing the VMs so as to minimize data access latencies while satisfying system constraints. We present optimal algorithms for determining the VM locations satisfying various constraints and with objectives that capture natural tradeoffs between minimizing latencies and incurring bandwidth costs. We also consider the problem of incorporating inter-VM latency constraints. In this case, the associated location problem is NP-hard with no effective approximation within a factor of 2 - ϵ for any ϵ > 0. We discuss an effective heuristic for this case and evaluate by simulation the impact of the various tradeoffs in the optimization objectives.
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paper_title: CCAP: A Cache Contention-Aware Virtual Machine Placement Approach for HPC Cloud

paper_content:

Applications in High Performance Computing (HPC) cloud are characterized by large cache resource consumption due to large-scale inputs and intensive communications, which creates serious Shared Last Level cache (SLLC) performance bottleneck. Current system software stacks are not efficient in addressing this issue among virtual machines at the hypervisor level or the threads at the operating system level. In this paper, we investigate performance interference due to contention for SLLC in the HPC cloud. We employ an enhanced reuse distance analysis technique with an accelerated cyclic compression algorithm to identify application's cache interference intensity. Based on reuse distance analysis, we propose a practical Cache Contention-Aware virtual machine Placement approach (CCAP). CCAP dispatches virtual machines according to their cache interference intensities to avoid cache pollution and interference, thus alleviating negative effects of cache contention. We implement CCAP in the Xen hypervisor. Evaluation of NPB workload reveals that CCAP can improve performance of cache sensitive applications when they are co-scheduled with cache pollution programs. For a 2-workload system, it reduces execution time by 12 %, as well as cache miss rate by 13 %, while increasing throughput by 13 %, on average. Moreover, CCAP also improves the average performance of the cache pollution programs by 5 %. For a 4-workload system, CCAP brings more significant performance improvement to cache sensitive applications, an average increase of 20 %.
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paper_title: Workload characteristics-aware virtual machine consolidation algorithms

paper_content:

An energy conservation strategy must address two issues — placement of virtual machine images and workload characteristics of virtual machines. For performance reason most cloud systems copy a prototype image into the local disk of a physical machine before starting a virtual machine. If the physical machine that stores the image of a virtual machine is off-line, then we cannot run this virtual machine. The workload characteristics of virtual machines determine whether it is data-intensive or CPU-intensive. We assume that the system has a distributed file system therefore a physical machine can run any virtual machine even if it does not have the image. However, we observe that the performance of a data-intensive virtual machine running on a physical machine without its image could result in 60% performance loss compared with running the same virtual machine on a physical machine that has the virtual machine image. On the other hand, the performance of a CPU-intensive virtual machine is almost independent of whether the physical machine has the image or not. As a result, an energy conservation algorithm must consider the workload characteristic of a virtual machine when finding a physical machine to run it, especially for data-intensive virtual machines. This paper proposes a workload characteristics-aware virtual machine consolidation algorithms. We propose an approximation algorithm and two dynamic programmings to consolidate virtual machines and reduce the number of physical machines. We conduct experiments and compare the numbers of physical machines used by our approximation algorithm with the optimal number of physical machines found by our dynamic programming. The experiment results indicate that our approximation algorithm finds good solutions much faster than the dynamic programming.
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paper_title: A Relationship-Based VM Placement Framework of Cloud Environment

paper_content:

Managing computation resources in a cost-effective way has become the core competence for a Cloud provider to win over the market because of the "pay-as-you-go" business model. Therefore, VM placement has become more and more important in the research and practices of VM management by determining at what condition and on which physical server a VM should be placed so that the SLA can be guaranteed and servers' utilization can be improved. Much existing work simply formulates the above issue to be a bin-packing problem, which does not take the VM relationships into account. However, the relationship information can greatly impact the SLA of the Cloud system and the resource utilization. Therefore, in this paper, we propose a relationship-based VM placement framework, SmartCRS, to optimize the VM placement procedure. SmartCRS reveals the relationships between VMs automatically. Then by using such information and based on a constraint library, it gives a proper VM placement plan. Finally, the plan is carried out by SmartCRS automatically or by Cloud administrators manually to improve the server utilization and guarantee the required SLA. Two case studies are conducted to demonstrate the effectiveness and efficiency of the proposed framework at the end of this paper.
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paper_title: PBFVMC: A New Pseudo-Boolean Formulation to Virtual-Machine Consolidation

paper_content:

Over the course of the last decade, there have been several improvements in the performance of Boolean Satisfiability (SAT), Integer Linear Programming (ILP) and Pseudo-Boolean Optimization (PBO) solvers. These improvements have encouraged the applications of SAT, ILP and PBO techniques in modeling complex engineering problems. One such problem is the Virtual Machine Consolidation. The Virtual Machine Consolidation problem consists in placing a set of virtual machines in a set of hardware in a way to increase workload on hardware where they can operate more energy-efficient. This paper proposes an improved PBO formulation of the Virtual Machine Consolidation problem, PBFVMC. The improved formulation and enhancements are built on top of a previous work and a new set of constraints is created and rationalized to work more friendly with current PBO solvers. It is observed that this new formulation goes a step ahead and more problems can now be solved.
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paper_title: QoS-Aware VM Placement in Multi-domain Service Level Agreements Scenarios

paper_content:

Virtualization technologies of Infrastructure-as-a- Service enable the live migration of running Virtual Machines (VMs) to achieve load balancing, fault-tolerance and hardware consolidation in data centers. However, the downtime/service unavailability due to live migration may be substantial with relevance to the customers' expectations on responsiveness, as the latter are declared in established Service Level Agreements (SLAs). Moreover, it may cause significant (potentially exponential) SLA violation penalties to its associated higher- level domains (Platform-as-a-Service and Software-as-a-Service). Therefore, VM live migration should be managed carefully. In this paper, we present the OpenStack version of the Generic SLA Manager, alongside its strategies for VM selection and allocation during live migration of VMs. We simulate a use case where IaaS (OpenStack-SLAM) and PaaS (OpenShift) are combined, and assess performance and efficiency of the aforementioned VM placement strategies, when a multi-domain SLA pricing & penalty model is involved. We find that our proposal is efficient in managing trade-offs between the operational objectives of service providers (including financial considerations) and the customers' expected QoS requirements.
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paper_title: A Network-aware Virtual Machine Placement and Migration Approach in Cloud Computing

paper_content:

Cloud computing represents a major step up in computing whereby shared computation resources are provided on demand. In such a scenario, applications and data thereof can be hosted by various networked virtual machines (VMs). As applications, especially data-intensive applications, often need to communicate with data frequently, the network I/O performance would affect the overall application performance significantly. Therefore, placement of virtual machines which host an application and migration of these virtual machines while the unexpected network latency or congestion occurs is critical to achieve and maintain the application performance. To address these issues, this paper proposes a virtual machine placement and migration approach to minimizing the data transfer time consumption. Our simulation studies suggest that the proposed approach is effective in optimizing the data transfer between the virtual machine and data, thus helping optimize the overall application performance.
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paper_title: Hierarchical Virtual Machine Consolidation in a Cloud Computing System

paper_content:

Improving the energy efficiency of cloud computing systems has become an important issue because the electric energy bill for 24/7 operation of these systems can be quite large. The focus of this paper is on the virtual machine (VM) consolidation in a cloud computing system as a way of lowering daily energy consumption of the system. In contrast to the existing works that assume resource demands of VMs are known and given as scalar variables, this paper treats these demands as random variables with known means and standard deviations. These random variables may be correlated with one another, and there are several kinds of resources which can be performance bottlenecks. Therefore, both the correlation and multiple resource type should be considered. The VM consolidation problem is then formulated as a multi-capacity stochastic bin packing problem. This problem is NP-hard, so we propose a heuristic method to solve the problem efficiently. The simulation results show that, in spite of its simplicity and scalability, the proposed method produces high quality solutions.
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paper_title: Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments

paper_content:

Server consolidation using virtualization technology has become increasingly important for improving data center efficiency. It enables one physical server to host multiple independent virtual machines (VMs), and the transparent movement of workloads from one server to another. Fine-grained virtual machine resource allocation and reallocation are possible in order to meet the performance targets of applications running on virtual machines. On the other hand, these capabilities create demands on system management, especially for large-scale data centers. In this paper, a two-level control system is proposed to manage the mappings of workloads to VMs and VMs to physical resources. The focus is on the VM placement problem which is posed as a multi-objective optimization problem of simultaneously minimizing total resource wastage, power consumption and thermal dissipation costs. An improved genetic algorithm with fuzzy multi-objective evaluation is proposed for efficiently searching the large solution space and conveniently combining possibly conflicting objectives. The simulation-based evaluation using power-consumption and thermal-dissipation models based on profiling of a Blade Center, demonstrates the good performance, scalability and robustness of our proposed approach. Compared with four well-known bin-packing algorithms and two single-objective approaches, the solutions obtained from our approach seek good balance among the conflicting objectives while others cannot.
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paper_title: Profiling Applications for Virtual Machine Placement in Clouds

paper_content:

Application profiling is an important technique for efficient resource management. The decision making of scheduling and resource allocation typically takes great advantage of such a technique primarily for improving resource utilization. With the advent of cloud computing as a multitenant virtualized platform, diverse applications are increasingly deployed onto the cloud and they more than often share physical resources. The background load (other applications running on the same physical machine) is therefore an important factor for profiling an application in this cloud computing scenario. In this paper, we present a novel application profiling technique using the canonical correlation analysis (CCA) method, which identifies the relationship between application performance and resource usage. We further devise a performance prediction model based on application profiles generated using CCA. Clearly, our profiling technique with this prediction model has a lot of potentials particularly in virtual machine (VM) placement with performance awareness. Our experimental results demonstrate the capability of our profiling technique and the accuracy of our prediction model.
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paper_title: Interference-aware virtual machine placement in cloud computing systems

paper_content:

Cloud computing provides scalable computing and storage resources. These scalable resources can be dynamically organized as many virtual machines (VMs) to run user applications based on a pay-per-use basis. This paper investigates the interference-aware VM placement (IAVMP) problem. In addition to fully exploiting the resources of PMs, the IAVMP problem considers the quality of service (QoS) requirements of user applications and the VM interference reduction. We first formulate the IAVMP problem by an Integer Linear Programming (ILP) model to solve it optimally. Due to the computation complexity of the ILP model, we also propose a polynomial-time heuristic algorithm to efficiently solve the IAVMP problem. Finally, simulation experiments are performed to demonstrate the effectiveness of the proposed heuristic algorithm by comparing with other VM placement algorithms.
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paper_title: Server consolidation with migration control for virtualized data centers

paper_content:

Virtualization has become a key technology for simplifying service management and reducing energy costs in data centers. One of the challenges faced by data centers is to decide when, how, and which virtual machines (VMs) have to be consolidated into a single physical server. Server consolidation involves VM migration, which has a direct impact on service response time. Most of the existing solutions for server consolidation rely on eager migrations, which try to minimize the number of physical servers running VMs. These solutions generate unnecessary migrations due to unpredictable workloads that require VM resizing. This paper proposes an LP formulation and heuristics to control VM migration, which prioritize virtual machines with steady capacity. We performed experiments using TU-Berlin and Google data center workloads to compare our migration control strategy against existing eager-migration-based solutions. We observed that avoiding migration of VMs with steady capacity reduces the number of migrations with minimal penalty in the number of physical servers.
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paper_title: Inter-and-intra data center VM-placement for energy-efficient large-Scale cloud systems

paper_content:

Data centers play the crucial role in the delivery of Cloud services by enabling on-demand access to the shared resources such as software, platform, and infrastructure. Virtual Machine (VM) allocation is one of the challenging tasks in the data center management since user requirements, typically expressed as SLAs, have to be met with the minimum operational expenditure. Despite their huge processing and storage facilities, data centers are among the major contributors of the GreenHouse Gas (GHG) emissions of the IT services. In this paper, we propose a holistic approach for a large-scale Cloud system where the Cloud services are provisioned by several data centers interconnected over the backbone network. We propose a Mixed Integer Linear Programming (MILP) formulation that aims at virtualizing the backbone topology and placing the VMs in data centers with the objective of minimum power consumption. We compare our proposed solution with a benchmark MILP model which selects the closest data centers that can accommodate the user requests taking into account the CPU, memory, and bandwidth capacities of the residing physical hosts. Collected experimental results show the benefit of the proposed management scheme in terms of power consumption, resource utilization, and fairness for medium size data centers.
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paper_title: Efficient VM placement with multiple deterministic and stochastic resources in data centers

paper_content:

Virtual machines (VMs) may significantly improve the efficiency of data center infrastructure by sharing resources of physical servers. This benefit relies on an efficient VM placement scheme to minimize the number of required servers. Existing VM placement algorithms usually assume that VMs' demands for resources are deterministic and stable. However, for certain resources, such as network bandwidth, VMs' demands are bursty and time varying, and demonstrate stochastic nature. In this paper, we study efficient VM placement in data centers with multiple deterministic and stochastic resources. First, we formulate the Multidimensional Stochastic VM Placement (MSVP) problem, with the objective to minimize the number of required servers and at the same time satisfy a predefined resource availability guarantee. Then, we show that the problem is NP-hard, and propose a polynomial time algorithm called Max-Min Multidimensional Stochastic Bin Packing (M3SBP). The basic idea is to maximize the minimum utilization ratio of all the resources of a server, while satisfying the demands of VMs for both deterministic and stochastic resources. Next, we conduct simulations to evaluate the performance of M3SBP. The results demonstrate that M3SBP guarantees the availability requirement for stochastic resources, and M3SBP needs the smallest number of servers to provide the guarantee among the benchmark algorithms.
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paper_title: Energy-Efficient Virtual Machine Replication and Placement in a Cloud Computing System

paper_content:

By utilizing Virtual Machines (VM) and doing server consolidation in a datacenter, a cloud provider can reduce the total energy consumption for servicing his clients with little performance degradation. In particular, the cloud provider can take advantage of dissimilar workloads and by assigning these workloads to the same server, can utilize fewer active servers to service his clients. Placing multiple copies of a VM on different servers and distributing the incoming requests among these VM copies can reduce the resource requirement for each VM copy and help the cloud provider utilize the servers more efficiently. In this paper, the problem of energy-efficient VM placement in a cloud computing system is solved. Precisely, we present an approach that first creates multiple copies of VMs and then uses dynamic programming and local search to place these copies on the physical servers. Simulation results show that the proposed algorithm reduces the total energy consumption by up to 20% with respect to previous work.
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paper_title: Optimizing VM placement for HPC in the cloud

paper_content:

Computing as a service model in cloud has encouraged High Performance Computing to reach out to wider scientific and industrial community. Many small and medium scale HPC users are exploring Infrastructure cloud as a possible platform to run their applications. However, there are gaps between the characteristic traits of an HPC application and existing cloud scheduling algorithms. In this paper, we propose an HPC-aware scheduler and implement it atop Open Stack scheduler. In particular, we introduce topology awareness and consideration for homogeneity while allocating VMs. We demonstrate the benefits of these techniques by evaluating them on a cloud setup on Open Cirrus test-bed.
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paper_title: Dynamic optimization of virtual machine placement by resource usage prediction

paper_content:

In order to save energy with preventing the lack of the resources on servers in data centers, we address the dynamic optimization of virtual machine placement. A decision problem of the virtual machine placement has been formulated as Bin Packing problem. Conventional methods repeat to solve the problem at regular intervals. Live migration is used for changing the virtual machine placement, but some of live migrations are unnecessary; the virtual machine is migrated repeatedly between the physical servers. For the purpose of reducing the unnecessary live migrations, we propose a dynamic optimization of virtual machine placement by resource usage prediction. The proposed method predicts the future resource usage by Auto Regressive Model. The proposed method decides the virtual machine placement by solving the Bin Packing problem with the predicted resource usage.
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paper_title: An Optimization-Based Scheme for Efficient Virtual Machine Placement

paper_content:

According to the important methodology of convex optimization theory, the energy-efficient and scalability problems of modern data centers are studied. Then a novel virtual machine (VM) placement scheme is proposed for solving these problems in large scale. Firstly, by referring the definition of VM placement fairness and utility function, the basic algorithm of VM placement which fulfills server constraints of physical machines is discussed. Then, we abstract the VM placement as an optimization problem which considers the inherent dependencies and traffic between VMs. By given the structural differences of recently proposed data center architectures, we further investigate a comparative analysis on the impact of the network architectures, server constraints and application dependencies on the potential performance gain of optimization-based VM placement. Comparing with the existing schemes, the performance improvements are illustrated from multiple perspectives, such as reducing the number of physical machines deployment, decreasing communication cost between VMs, improving energy-efficient and scalability of data centers.
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paper_title: Improved energy-efficiency in cloud datacenters with interference-aware virtual machine placement

paper_content:

Virtualization is one of the main technologies used for improving resource efficiency in datacenters; it allows the deployment of co-existing computing environments over the same hardware infrastructure. However, the co-existing of environments — along with management inefficiencies — often creates scenarios of high-competition for resources between running workloads, leading to performance degradation. This phenomenon is known as Performance Interference, and introduces a non-negligible overhead that affects both a datacenter's Quality of Service and its energy-efficiency. This paper introduces a novel approach to workload allocation that improves energy-efficiency in Cloud datacenters by taking into account their workload heterogeneity. We analyze the impact of performance interference on energy-efficiency using workload characteristics identified from a real Cloud environment, and develop a model that implements various decision-making techniques intelligently to select the best workload host according to its internal interference level. Our experimental results show reductions in interference by 27.5% and increased energy-efficiency up to 15% in contrast to current mechanisms for workload allocation.
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paper_title: Energy-aware virtual machine placement in data centers

paper_content:

The energy efficiency of modern data centers has become a practical concern and has attracted significant attention in recent years. In contract to existing solutions that primarily focuses on only one specific aspect of management to reduce energy consumption, this paper explores the balance between server energy consumption and network energy consumption to present an energy-aware joint virtual machine (VM) placement. Given the definition of VM placement fairness, the basic algorithm of VM placement which fulfills server energy consumption constraints is conducted. Then, we further formulate the VM placement as an optimization problem which considers application dependencies to reduce network energy consumption. We design a joint algorithm that efficiently solves the VM placement problem for very large problem sizes. Using simulations, we conduct a comparative analysis on the impact of the data center architectures, server constraints and application dependencies on the potential performance gain of energy-aware VM placement. Compared to existing generic methods, we show a significant performance improvement such as efficiently reducing the number of physical machines used to save server energy consumption, decreasing the communication distance between VMs to obtain data center network energy consumption efficiency, improving scalability of data centers.
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paper_title: A network-aware virtual machine placement algorithm in mobile cloud computing environment

paper_content:

By taking into consideration network features of a wireless mesh network environment where mobile cloud computing (MCC) system is deployed, this paper proposes an effective virtual machine (VM) placement algorithm. This algorithm tends to place a newly created VM for a cloud service to a cloud server, which itself is also a mobile device, that occurs lowest network delay when accessing to application data stored in wireline networks. The aim is to reduce cloud service response time as much as possible in order to deliver better quality of experience to cloud end users over more noisy and less robust wireless networks. In addition, the proposed deployment of cloud storage and cloud computational resources onto wireless networks is also a contribution of the paper. The simulation results have shown the effectiveness and the efficiency of the proposed VM placement algorithms both in terms of cloud service response time.
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paper_title: Migration-based virtual machine placement in cloud systems

paper_content:

Cloud computing is an emerging technology that greatly shapes our lives, where users run jobs on virtual machines (VMs) on physical machines (PMs) provided by a cloud provider, saving the investment in upfront infrastructures. Due to the heterogeneity of various jobs, different VMs on the same PM can have different job completion times. Meanwhile, the PMs are also heterogeneous. Therefore, different VM placements have different job completion times. Our objective is to minimize the total job completion time of the input VM requests through a reasonable VM placement schedule. This problem is NP-hard, since it can be reduced to a knapsack problem. We propose an off-line VM placement method through emulated VM migration, while the on-line VM placement is solved by a real VM migration process. The migration algorithm is a heuristic approach, where we place the VM to its best PM directly, as long as it has enough capacity. Otherwise, if the migration constraint is satisfied, we migrate another VM from this PM to accommodate the new VM. Furthermore, we study a hybrid scheme where a batch is employed to accept upcoming VMs for the on-line scenario. Evaluation results prove the high efficiency of our algorithms.
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paper_title: An Energy Efficient Virtual Machine Placement Algorithm with Balanced Resource Utilization

paper_content:

Virtualization is a key technology for resource sharing in IaaS/PaaS cloud infrastructures. One primary issue in virtualization is the virtual machine placement (VMP) problem, which is to choose proper physical machine (PM) to deploy virtual machines (VMs) in runtime. In this paper, we study the VMP problem with the goal of minimizing the total energy consumption. We first present a multi-dimensional space partition model to characterize the resource usage states of PMs. Based on this model, we then propose a virtual machine placement algorithm, which can balance the utilization of multi-dimensional resources, reduce the number of running PMs and thus lower down the energy consumption. We also evaluate our proposed balanced algorithm via extensive simulations. Simulation results show that our approach can save as much as 15% energy compared to the first fit algorithm over a long run.
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paper_title: Reducing electricity cost through virtual machine placement in high performance computing clouds

paper_content:

In this paper, we first study the impact of load placement policies on cooling and maximum data center temperatures in cloud service providers that operate multiple geographically distributed data centers. Based on this study, we then propose dynamic load distribution policies that consider all electricity-related costs as well as transient cooling effects. Our evaluation studies the ability of different cooling strategies to handle load spikes, compares the behaviors of our dynamic cost-aware policies to cost-unaware and static policies, and explores the effects of many parameter settings. Among other interesting results, we demonstrate that (1) our policies can provide large cost savings, (2) load migration enables savings in many scenarios, and (3) all electricity-related costs must be considered at the same time for higher and consistent cost savings.
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paper_title: An energy-efficient approach for virtual machine placement in cloud based data centers

paper_content:

Cloud computing is a new technology which is proffering IT services based on pay-as-you-go model to consumers from everywhere in the world. The growing demand of Cloud infrastructure and modern computational requests like business, scientific and web applications result in large-scale data centers and lead to extra electrical energy consumption. High energy consumption causes high operational cost and also led to high carbon emission which is harmful for atmosphere. Hence, energy-efficient techniques are required to minimize the negative effects of Cloud computing on the environment. Virtual machines (VMs) migration, dynamic consolidation in the virtualized data centers in cloud environments and switching idle physical machines off could yield reduce energy consumption; hence, one of the important issues for energy efficiency in virtualized cloud environments is how to place new VMs or selected VMs for migration across the hosts. In this paper we propose an energy-efficient approach based on Minimum Correlation Coefficient (MCC) method for virtual machine placement in cloud based, virtualized data centers. The proposed approach regards both Service Level Agreement (SLA) and low energy consumption and tries to make a trade-off between these two concerns using fuzzy Analytic Hierarchy Process (AHP). We evaluate our approach using Cloudsim toolkit as a modern cloud computing environment simulator. The evaluation shows that our proposed method offers a suitable trade-off between power efficiency and SLA violation reduction in cloud data centers.
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paper_title: Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement

paper_content:

The scalability of modern data centers has become a practical concern and has attracted significant attention in recent years. In contrast to existing solutions that require changes in the network architecture and the routing protocols, this paper proposes using traffic-aware virtual machine (VM) placement to improve the network scalability. By optimizing the placement of VMs on host machines, traffic patterns among VMs can be better aligned with the communication distance between them, e.g. VMs with large mutual bandwidth usage are assigned to host machines in close proximity. We formulate the VM placement as an optimization problem and prove its hardness. We design a two-tier approximate algorithm that efficiently solves the VM placement problem for very large problem sizes. Given the significant difference in the traffic patterns seen in current data centers and the structural differences of the recently proposed data center architectures, we further conduct a comparative analysis on the impact of the traffic patterns and the network architectures on the potential performance gain of traffic-aware VM placement. We use traffic traces collected from production data centers to evaluate our proposed VM placement algorithm, and we show a significant performance improvement compared to existing general methods that do not take advantage of traffic patterns and data center network characteristics.
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paper_title: Joint VM placement and routing for data center traffic engineering

paper_content:

Today's data centers need efficient traffic management to improve resource utilization in their networks. In this work, we study a joint tenant (e.g., server or virtual machine) placement and routing problem to minimize traffic costs. These two complementary degrees of freedom—placement and routing—are mutually-dependent, however, are often optimized separately in today's data centers. Leveraging and expanding the technique of Markov approximation, we propose an efficient online algorithm in a dynamic environment under changing traffic loads. The algorithm requires a very small number of virtual machine migrations and is easy to implement in practice. Performance evaluation that employs the real data center traffic traces under a spectrum of elephant and mice flows, demonstrates a consistent and significant improvement over the benchmark achieved by common heuristics.
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paper_title: Definition of Security Metrics for the Cloud Computing and Security-Aware Virtual Machine Placement Algorithms

paper_content:

Nowadays, Cloud Computing is becoming a key factor in computer science. Besides the great benefits it brought to the information technology and to the economy, Cloud Computing shows some weakness when looking at the security. An IaaS client should be able to specify its security requirements. But the lack of a system of security metrics leads to the incapability of quantifying the security level of a client deployment in a Cloud. Therefore, we propose a system of security metrics specific to the Cloud Computing and use it to develop virtual machines placement algorithms.
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paper_title: VMPlanner : Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers

paper_content:

In recent years, the power costs of cloud data centers have become a practical concern and have attracted significant attention from both industry and academia. Most of the early works on data center energy efficiency have focused on the biggest power consumers (i.e., computer servers and cooling systems), yet without taking the networking part into consideration. However, recent studies have revealed that the network elements consume 10-20% of the total power in the data center, which poses a great challenge to effectively reducing network power cost without adversely affecting overall network performance. Based on the analysis on topology characteristics and traffic patterns of data centers, this paper presents a novel approach, called VMPlanner, for network power reduction in the virtualization-based data centers. The basic idea of VMPlanner is to optimize both virtual machine placement and traffic flow routing so as to turn off as many unneeded network elements as possible for power saving. We formulate the optimization problem, analyze its hardness, and solve it by designing VMPlanner as a stepwise optimization approach with three approximation algorithms. VMPlanner is implemented and evaluated in a simulated environment with traffic traces collected from a data center test-bed, and the experiment results illustrate the efficacy and efficiency of this approach.
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paper_title: On Theory of VM Placement: Anomalies in Existing Methodologies and Their Mitigation Using a Novel Vector Based Approach

paper_content:

In this paper, we present the methodologies used in existing literature for Virtual Machine (VM) placement, load balancing and server consolidation in a data center environment. While the methodologies may seem fine on the surface, certain drawbacks and anomalies can be uncovered when they are analyzed deeper. We point out those anomalies and draw backs in the existing literature and explain what are the root causes of such anomalies. Then we propose a novel methodology based on vector arithmetic which not only addresses those anomalies but also leads to some interesting theories and algorithms to tackle the above mentioned three functionalities required in managing resources of data centers. We believe that with a strong mathematical base, our methodology has the potential to become the foundation of future models and algorithms in this research area.
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paper_title: Towards Profitable Virtual Machine Placement in the Data Center

paper_content:

Motivated by the limit on the power usage effectiveness (PUE) of the data centers, the potential benefit of the consolidation, and the impetus of achieving maximum return on investment (ROI) on the cloud computing market, we investigate VM placement in the data center, formulate a multi-level generalized assignment problem (MGAP) for maximizing the profit under the service level agreement and the power budget constraint based on the model of a virtualized data center, and solve it with a first-fit heuristic. Numerical simulations show that the first-fit heuristic is effective in solving the large-scale instances of the MGAP with the sampled simulation setups.
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paper_title: SLA guaranteed virtual machine consolidation for computing clouds

paper_content:

One of the most attractive features that computing clouds can offer is that the data center maintenance cost can be reduced by consolidating the workload generated by the virtual machines (VMs) into a few powerful physical servers. In this paper, the problem of maximally consolidating heterogeneous virtual machines into servers while protecting the service level agreement of each virtual machine is investigated. A generic and robust VM workload consolidation framework is proposed. The proposed framework achieves several attractive properties like low maintenance cost, low algorithm complexity, adjustable resource provisioning aggressiveness, and good scalability.
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paper_title: Prevent VM Migration in Virtualized Clusters via Deadline Driven Placement Policy

paper_content:

VM consolidation has been shown as a promising technique for saving energy costs of a data center. It relies on VM migration to move user applications or jobs onto fewer numbers of physical servers during off peak hour. However, VM migration is a costly operation that could cause several concerns, such as performance degradation and system instability. Most existing works were proposed to minimize the migration cost for dynamic consolidation which migrates VM at the runtime when SLA violation or resource under-utilization is detected. In contrast, this paper aims to proactively prevent VM migration for semi-static VM consolidation by proposing a deadline driven VM placement strategy based on the awareness of the server turn-off time and job execution time. We evaluate our approach using a real HPC cluster trace as well as a set of synthetic generated workloads. The results show our approach can significantly reduce the number of migrations by 70% on the real trace. We also demonstrate that our approach can be resilient to different workload patterns by achieving consistent improvement around 50% over all the synthetic workloads.
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paper_title: A Two Stage Heuristic Algorithm for Solving the Server Consolidation Problem with Item-Item and Bin-Item Incompatibility Constraints

paper_content:

The problem of server sprawl is common in data centers of most business organizations. It is most often the case that an application is run on dedicated servers. This leads to situations where organizations end up having numerous servers that remain under-utilized most of the times. The servers, in such scenarios, are allocated more resources (disk, cpu and memory) than are justified by their present workloads. Consolidating multiple underutilized servers into a fewer number of non-dedicated servers that can host multiple applications is an effective tool for businesses to enhance their returns on investment. The problem can be modeled as a variant of the bin packing problem where items to be packed are the servers being consolidated and bins are the target servers. The sizes of the servers/items being packed are resource utilizations which are obtained from the performance trace data. Here we describe a novel two stage heuristic algorithm for taking care of the "bin-item" incompatibility constraints that are inherent in any server consolidation problem. The model is able to solve extremely large instances of problem in a reasonable amount of time.
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paper_title: Virtual machine placement for predictable and time-constrained peak loads

paper_content:

We present an approach to optimal virtual machine placement within datacenters for predicable and time-constrained load peaks. A method for optimal load balancing is developed, based on binary integer programming. For tradeoffs between quality of solution and computation time, we also introduce methods to pre-process the optimization problem before solving it. Upper bound based optimizations are used to reduce the time required to compute a final solution, enabling larger problems to be solved. For further scalability, we also present three approximation algorithms, based on heuristics and/or greedy formulations. The proposed algorithms are evaluated through simulations based on synthetic data sets. The evaluation suggests that our algorithms are feasible, and that these can be combined to achieve desired tradeoffs between quality of solution and execution time.
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paper_title: Time-Aware VM-Placement and Routing with Bandwidth Guarantees in Green Cloud Data Centers

paper_content:

Variation in network performance due to the shared resources is a key obstacle for cloud adoption. Thus, the success of cloud providers to attract more tenants depends on their ability to provide bandwidth guarantees. Power efficiency in data centers has become critically important for supporting larger number of tenants. In this paper, we address the problem of time-aware VM-placement and routing (TVPR), where each tenant requests for a specified amount of server resources (VMs) and network resource (bandwidth) for a given duration. The TVPR problem allocates the required resources for as many tenants as possible by finding the right set of servers to map their VMs and routing their traffic so as to minimize the total power consumption. We propose a multi-component utilization-based power model to determine the total power consumption of a data center according to the resource utilization of the components (servers and switches). We then develop a mixed integer linear programming (MILP) optimization problem formulation based on the proposed power model and prove it to be N P-complete. Since the TVPR problem is computationally prohibitive, we develop a fast and scalable heuristic algorithm. To demonstrate the efficiency of our proposed algorithm, we compare its performance with the numerical results obtained by solving the MILP problem using CPLEX, for a small data center. We then demonstrate the effectiveness of the proposed algorithm in terms of power consumption and acceptance ratio for large data centers through simulation results.
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paper_title: Online traffic-aware virtual machine placement in data center networks

paper_content:

Data centers rely on virtualization to provide different services over a shared infrastructure. The placement of the different services and tasks in the physical machines is crucial for the performance of the whole system. A misplaced service can overload some network links, lead to congestion, or even connection disruptions. On the other hand, virtual machine migration allows reallocating services and changing the traffic matrix, leading to more efficient use of bandwidth. In this paper, we propose a Virtual Machine Placement (VMP) algorithm to (re)allocate virtual machines in the data center servers, based on the current traffic matrix, CPU, and memory usage. Analyzing the formation of community patterns in terms of traffic using graph theory, we are able to find virtual machines that are correlated because they exchange high amount of data. Those virtual machines are aggregated and allocated to servers as close as possible to each other, reducing traffic congestion. Our simulation results show that VMP was able to improve the traffic distribution. In some specific cases we were able to reduce 80% of the core traffic, concentrating it at the edge of the network.
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paper_title: Virtual Machine Placement Optimization Supporting Performance SLAs

paper_content:

Cloud computing model separates usage from ownership in terms of control on resource provisioning. Resources in the cloud are projected as a service and are realized using various service models like IaaS, PaaS and SaaS. In IaaS model, end users get to use a VM whose capacity they can specify but not the placement on a specific host or with which other VMs it can be co-hosted. Typically, the placement decisions happen based on the goals like minimizing the number of physical hosts to support a given set of VMs by satisfying each VMs capacity requirement. However, the role of the VMM usage to support I/O specific workloads inside a VM can make this capacity requirement incomplete. I/O workloads inside VMs require substantial VMM CPU cycles to support their performance. As a result, placement algorithms need to include the VMM's usage on a per VM basis. Secondly, cloud centers encounter situations wherein change in existing VM's capacity or launching of new VMs need to be considered during different placement intervals. Usually, this change is handled by migrating existing VMs to meet the goal of optimal placement. We argue that VM migration is not a trivial task and does include loss of performance during migration. We quantify this migration overhead based on the VM's workload type and include the same in placement problem. One of the goals of the placement algorithm is to reduce the VM's migration prospects, thereby reducing chances of performance loss during migration. This paper evaluates the existing ILP and First Fit Decreasing (FFD) algorithms to consider these constraints to arrive at placement decisions. We observe that ILP algorithm yields optimal results but needs long computing time even with parallel version. However, FFD heuristics are much faster and scalable algorithms that generate a sub-optimal solution, as compared to ILP, but in time-scales that are useful in real-time decision making. We also observe that including VM migration overheads in the placement algorithm results in a marginal increase in the number of physical hosts but a significant, of about 84 percent reduction in VM migration.
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paper_title: An Availability-Aware Virtual Machine Placement Approach for Dynamic Scaling of Cloud Applications

paper_content:

Cloud computing promises customers the on-demand ability to dynamically provision virtualization resources in face of workload variations. Most existing scaling approaches addressed this problem by allocating application to a certain amount of cloud resources. However, the problem of the availability of application influenced by VM-based physical locations during resource scaling process is a serious challenge due to dynamic complex workload and has not been widely discussed yet. In this paper, we present a novel availability-based computing model to describe availability attribute of one application in the hierarchical structured cloud. Moreover, we propose an availability-aware policy by performing both vertical and horizontal scaling to explore how and where to allocate computing resource. Simulation results indicate that our model captured the availability of cloud applications properly and proposed scaling approach achieves the objectives of meeting availability demands and minimizing the total communication cost.
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paper_title: Improving Performance and Availability of Services Hosted on IaaS Clouds with Structural Constraint-Aware Virtual Machine Placement

paper_content:

The increasing popularity of modern virtualization-based datacenters continues to motivate both industry and academia to provide answers to a large variety of new and challenging questions. In this paper we aim to answer focusing on one such question: how to improve performance and availability of services hosted on IaaS clouds. Our system, structural constraint-aware virtual machine placement (SCAVP), supports three types of constraints: demand, communication and availability. We formulate SCAVP as an optimization problem and show its hardness. We design a hierarchical placement approach with four approximation algorithms that efficiently solves the SCAVP problem for large problem sizes. We provide a formal model for the application (to better understand structural constraints) and the datacenter (to effectively capture capabilities), and use the two models as inputs to the placement problem. We evaluate SCAVP in a simulated environment to illustrate the efficiency and importance of the proposed approach.
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paper_title: An energy-aware heuristic framework for virtual machine consolidation in Cloud computing

paper_content:

Virtual machine (VM) consolidation in Cloud computing provides a great opportunity for energy saving. However, the obligation of providing suitable quality of service to end users leads to the necessity in dealing with energy-performance tradeoff. In this paper, we propose a redesigned energy-aware heuristic framework for VM consolidation to achieve a better energy-performance tradeoff. There are two main contributions in the framework: (1) establish a service level agreement (SLA) violation decision algorithm to decide whether a host is overload with SLA violation; (2) minimum power and maximum utilization policy is then proposed to improve the Minimum Power policy in previous work. Finally, we have evaluated our framework through simulation on large-scale experiments driven by workload traces from more than a thousand VMs, and the results show that our framework outperforms previous work. Specifically, it guarantees 21---34 % decrease in energy consumption, 84---92 % decrease in SLA violation, 87---94 % decrease in energy-performance metric, and 63 % decrease in execution time. And we further discuss why the redesigned framework outperforms the previous design.
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paper_title: VM Placement Strategies for Cloud Scenarios

paper_content:

The problem of Virtual Machine (VM) placement in a compute cloud infrastructure is well-studied in the literature. However, the majority of the existing works ignore the dynamic nature of the incoming stream of VM deployment requests that continuously arrive to the cloud provider infrastructure. In this paper we provide a practical model of cloud placement management under a stream of requests and present a novel technique called Backward Speculative Placement (BSP)that projects the past demand behavior of a VM to a candidate target host. We exploit the BSP technique in two algorithms, first for handling the stream of deployment requests, second in a periodic optimization, to handle the dynamic aspects of the demands. We show the benefits of our BSPtechnique by comparing the results on a simulation period with a strategy of choosing an optimal placement at each time instant, produced by a generic MIP solver.
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paper_title: Redundant virtual machine placement for fault-tolerant consolidated server clusters

paper_content:

Consolidated server systems using server virtualization involves serious risks of host server failures that induce unexpected downs of all hosted virtual machines and applications. To protect applications requiring high-availability from unpredictable host server failures, redundant configuration using virtual machines can be an effective countermeasure. This paper presents a virtual machine placement method for establishing a redundant configuration against host server failures with less host servers. The proposed method estimates the requisite minimum number of virtual machines according to the performance requirements of application services and decides an optimum virtual machine placement so that minimum configurations survive at any k host server failures. The evaluation results clarify that the proposed method achieves requested fault-tolerance level with less number of hosting servers compared to the conventional N+M redundant configuration approach.
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paper_title: Elasticity-aware virtual machine placement for cloud datacenters

paper_content:

With the increasing popularity of cloud computing, the cloud datacenter suffers from both limited resources and the variation of users' requests. One important feature of cloud computing is on-demand scaling, enabling the fluctuation of one user's resource demand. However, amongst previous work concerning the virtual machine (VM) placement in datacenters, satisfying the VMs' requested resources of users is the primary objective, neglecting future demand variation. In this paper, we propose the concept of elasticity, referring to how well the datacenter can satisfy the growth of the input VMs resource demands under both the limitations of physical machines (PMs) capacities and links capacities. To consider both dimensions of the machine and bandwidth resources simultaneously, we propose our hierarchical VM placement algorithm. We also prove the optimality of our algorithm in a frequently used semi-homogeneous datacenter configuration. Furthermore, we study the heterogeneous datacenter configuration, favoring the characteristics of multi-tenant datacenters. Evaluation results validate the efficiency of our algorithm.
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paper_title: Virtual Machine Placement for Improving Energy Efficiency and Network Performance in IaaS Cloud

paper_content:

Under the premise of ensuring application performance, how to place virtual machines (VMs) on physical machines (PMs) to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers in IaaS cloud. The existing VM placement schemes are mostly to reduce energy consumption by optimizing utilization of physical server or network elements, but the issue of aggressive consolidation of VM is ignored, which may lead to network performance degradation. To address the issue, this paper proposes a VM placement scheme based on a new two-stage heuristic algorithm to optimize network performance and to reduce energy consumption of physical servers and network elements, so as to finally achieve the tradeoff between energy efficiency and network performance. The simulations show that our solution achieves good results.
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paper_title: Energy-Saving Virtual Machine Placement in Cloud Data Centers

paper_content:

In cloud data centers, different mapping relationships between virtual machines (VMs) and physical machines (PMs) cause different resource utilization, therefore, how to place VMs on PMs to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers. The existing VM placement schemes are to optimize physical server resources utilization or network resources utilization, but few of them focuses on optimizing multiple resources utilization simultaneously. To address the issue, this paper proposes a VM placement scheme meeting multiple resource constraints, such as the physical server size (CPU, memory, storage, bandwidth, etc.) and network link capacity to improve resource utilization and reduce both the number of active physical servers and network elements so as to finally reduce energy consumption. Since VM placement problem is abstracted as a combination of bin packing problem and quadratic assignment problem, which is also known as a classic combinatorial optimization and NP-hard problem, we design a novel greedy algorithm by combining minimum cut with the best-fit, and the simulations show that our solution achieves better results.
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paper_title: Shadow-routing based dynamic algorithms for virtual machine placement in a network cloud

paper_content:

We consider a shadow routing based approach to the problem of real-time adaptive placement of virtual machines (VM) in large data centers (DC) within a network cloud. Such placement in particular has to respect vector packing constraints on the allocation of VMs to host physical machines (PM) within a DC, because each PM can potentially serve multiple VMs simultaneously. Shadow routing is attractive in that it allows a large variety of system objectives and/or constraints to be treated within a common framework (as long as the underlying optimization problem is convex). Perhaps even more attractive feature is that the corresponding algorithm is very simple to implement, it runs continuously, and adapts automatically to changes in the VM demand rates, changes in system parameters, etc., without the need to re-solve the underlying optimization problem “from scratch”. In this paper we focus on the minmax-DC-load problem. Namely, we propose a combined VM-toDC routing and VM-to-PM assignment algorithm, referred to as Shadow scheme, which minimizes the maximum of appropriately defined DC utilizations. We prove that the Shadow scheme is asymptotically optimal (as one of its parameters goes to 0). Simulation confirms good performance and high adaptivity of the algorithm. Favorable performance is also demonstrated in comparison with a baseline algorithm based on VMware implementation [7], [8]. We also propose a simplified - “more distributed” - version of the Shadow scheme, which performs almost as well in simulations.
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paper_title: A Mathematical Programming Approach for Server Consolidation Problems in Virtualized Data Centers

paper_content:

Today's data centers offer IT services mostly hosted on dedicated physical servers. Server virtualization provides a technical means for server consolidation. Thus, multiple virtual servers can be hosted on a single server. Server consolidation describes the process of combining the workloads of several different servers on a set of target servers. We focus on server consolidation with dozens or hundreds of servers, which can be regularly found in enterprise data centers. Cost saving is among the key drivers for such projects. This paper presents decision models to optimally allocate source servers to physical target servers while considering real-world constraints. Our central model is proven to be an NP-hard problem. Therefore, besides an exact solution method, a heuristic is presented to address large-scale server consolidation projects. In addition, a preprocessing method for server load data is introduced allowing for the consideration of quality-of-service levels. Extensive experiments were conducted based on a large set of server load data from a data center provider focusing on managerial concerns over what types of problems can be solved. Results show that, on average, server savings of 31 percent can be achieved only by taking cycles in the server workload into account.
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paper_title: A virtual machine consolidation framework for MapReduce enabled computing clouds

paper_content:

In nowadays computing clouds, it is of the cloud providers' economic interests to correctly consolidate the workload of the virtual machines (VMs) into the suitable physical servers in the cloud data center in order to minimize the total maintenance cost. However, during the consolidation process, sufficient protection should be provided to the service level agreement (SLA) of the VMs. In this paper, the VM consolidation problem for MapReduce enabled computing clouds has been investigated. In the MapReduce enabled computing clouds, MapReduce jobs are carried out by homogeneous MapReduce VM instances that have identical hardware resource. Two resource allocation schemes with corresponding SLA constraints for the MapReduce VMs and the non-MapReduce VMs are proposed. Based on these schemes, the VM consolidation problem is modeled as an integer nonlinear optimization problem and an efficient algorithm has been proposed to locate its solutions. The results show that better VM consolidation performance can be achieved by colocating MapReduce instances together with non-MapReduce instances in the same set of physical servers.
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paper_title: Reduce Energy Consumption through Virtual Machine Placement in Cloud Data Centre

paper_content:

In this paper, energy consumption in the data centre was studied where thousands of servers and other devices runs, energy are utilized to run the server and cooling the environment. Energy consumption can be reduced by switching off the idle server by means of migration of Virtual Machine from under-load Host. Load in cloud computing is maintained by migration of VM from the overloaded Host to a free Host or activate new Host. Based on this study, a reservation technique by using BIN packing was proposed in this paper with an overload detection algorithm. The proposed algorithm RBIN is experimented in 800 servers with 1024 Virtual Machines. From the experimental result, proposed method RBIN reduces energy in higher level.
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paper_title: CCAP: A Cache Contention-Aware Virtual Machine Placement Approach for HPC Cloud

paper_content:

Applications in High Performance Computing (HPC) cloud are characterized by large cache resource consumption due to large-scale inputs and intensive communications, which creates serious Shared Last Level cache (SLLC) performance bottleneck. Current system software stacks are not efficient in addressing this issue among virtual machines at the hypervisor level or the threads at the operating system level. In this paper, we investigate performance interference due to contention for SLLC in the HPC cloud. We employ an enhanced reuse distance analysis technique with an accelerated cyclic compression algorithm to identify application's cache interference intensity. Based on reuse distance analysis, we propose a practical Cache Contention-Aware virtual machine Placement approach (CCAP). CCAP dispatches virtual machines according to their cache interference intensities to avoid cache pollution and interference, thus alleviating negative effects of cache contention. We implement CCAP in the Xen hypervisor. Evaluation of NPB workload reveals that CCAP can improve performance of cache sensitive applications when they are co-scheduled with cache pollution programs. For a 2-workload system, it reduces execution time by 12 %, as well as cache miss rate by 13 %, while increasing throughput by 13 %, on average. Moreover, CCAP also improves the average performance of the cache pollution programs by 5 %. For a 4-workload system, CCAP brings more significant performance improvement to cache sensitive applications, an average increase of 20 %.
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paper_title: An energy aware framework for virtual machine placement in cloud federated data centres

paper_content:

Data centres are powerful ICT facilities which constantly evolve in size, complexity, and power consumption. At the same time users' and operators' requirements become more and more complex. However, existing data centre frameworks do not typically take energy consumption into account as a key parameter of the data centre's configuration. To lower the power consumption while fulfilling performance requirements we propose a flexible and energy-aware framework for the (re)allocation of virtual machines in a data centre. The framework, being independent from the data centre management system, computes and enacts the best possible placement of virtual machines based on constraints expressed through service level agreements. The framework's flexibility is achieved by decoupling the expressed constraints from the algorithms using the Constraint Programming (CP) paradigm and programming language, basing ourselves on a cluster management library called Entropy. Finally, the experimental and simulation results demonstrate the effectiveness of this approach in achieving the pursued energy optimization goals.
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paper_title: EQVMP: Energy-efficient and QoS-aware virtual machine placement for software defined datacenter networks

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To provide effective and reliable services, cloud datacenters need parallel computing and virtualization techniques. This work presents an improved virtual machine (VM) placement mechanism, called Energy efficiency and Quality of Service (QoS) aware VM Placement (EQVMP) to overcome the problem of unbalanced traffic load in switching on and off VMs for the purpose of energy saving. EQVMP combines three key techniques: (1) hop reduction, (2) energy saving and (3) load balancing. Hop reduction can regroup VMs to lower the traffic load among them. Energy saving techniques aim at choosing the appropriate servers. The proposed load balancing updates VM placement periodically. Our experimental results show that the proposed scheme can lower energy consumption and maintain QoS. We propose an evaluation score [1] to assess VM placement in terms of energy, delay and throughput. Comparing to other existing placement policies, our proposed mechanism can enhance system throughput by 25% and can have better evaluation score.
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paper_title: Optimal update frequency model for physical machine state change and virtual machine placement in the cloud

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Cloud computing is evolving into the default operational framework running modern data centers. Efficient data center operation is concerned with the total amount of energy consumed as well as assuring adequate resources are available to process all of the incoming work requests. Existing research has demonstrated several algorithms that can be used to determine the optimal number of resources required to service these requests. However, a key issue not addressed in these algorithms is determining the frequency of recalculating the number of required resources. Changing the required resources at a rate slower than the optimal update frequency results in lower energy efficiency due to the over allocation of resources. Changing the resources at a rate higher than the optimal frequency results in insufficient time for systems to change state, which results in SLA violations. In this paper, a stochastic optimization model is presented that determines the optimal update frequency for changing the states of the nodes of the cloud as well as determining the proper frequency for recalculating the maximum expected load, which improves the determination of the optimum number of resources required, therefore maximizes energy efficiency and minimizes SLA violations.
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paper_title: Network-aware virtual machine consolidation for large data centers

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Resource management in modern data centers has become a challenging task due to the tremendous growth of data centers. In large virtual data centers, performance of applications is highly dependent on the communication bandwidth available among virtual machines. Traditional algorithms either do not consider network I/O details of the applications or are computationally intensive. We address the problem of identifying the virtual machine clusters based on the network traffic and placing them intelligently in order to improve the application performance and optimize the network usage in large data center. We propose a greedy consolidation algorithm that ensures the number of migrations is small and the placement decisions are fast, which makes it practical for large data centers. We evaluated our approach on real world traces from private and academic data centers, using simulation and compared the existing algorithms on various parameters like scheduling time, performance improvement and number of migrations. We observed a ~70% savings of the interconnect bandwidth and overall ~60% improvements in the applications performances. Also, these improvements were produced within a fraction of scheduling time and number of migrations.
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paper_title: Energy Efficient VM Placement Supported by Data Analytic Service

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The popularity and commercial use of cloud computing has prompted an increased concern among cloud service providers for energy efficiency while still maintaining quality of service. One of the key techniques used for the efficient use of cloud server resources is virtual machine placement. This work introduces a precise VM placement algorithm that ensures energy efficiency and also prevents Service Level Agreement (SLA) violation. The mathematical model of the algorithm is supported by a sophisticated data analytic system implemented as a service. The precision of the algorithm is achieved by allowing each individual VM to build its own data model on demand over an appropriate time horizon. Thus the data model can reflect accurately the characteristics of resource usage of the VM. The algorithm can communicate synchronously or asynchronously with the data analytic service which is deployed as a cloud-based solution. In the experiments, several advanced data modelling and use forecasting techniques were evaluated. Results from simulation-based experiments show that the VM placement algorithm (supported by the data analytic service) can effectively reduce power consumption, the number of VM migrations, and prevent SLA violation, it also compares very favourably with other placement algorithms.
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paper_title: Maximum migration time guarantees in dynamic server consolidation for virtualized data centers

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Server consolidation is a vital mechanism in modern data centers in order to minimize expenses with infrastructure. In most cases, server consolidation may require migrating virtual machines between different physical servers. Although the downtime of live-migration is negligible, the amount of time to migrate all virtual machines can be substantial, delaying the completion of the consolidation process. This paper proposes a new server consolidation algorithm, which guarantees that migrations are completed in a given maximum time. The migration time is estimated using the max-min fairness model, in order to consider the competition of migration flows for the network infrastructure. The algorithm was simulated using a real workload and shows a good consolidation ratio in comparison to other algorithms, while also guaranteeing a maximum migration time.
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paper_title: A Stable Network-Aware VM Placement for Cloud Systems

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Virtual Machine (VM) placement has to carefully consider the aggregated resource consumption of co-located VMs in order to obey service level agreements at lower possible cost. In this paper, we focus on satisfying the traffic demands of the VMs in addition to CPU and memory requirements. This is a much more complex problem both due to its quadratic nature (being the communication between a pair of VMs) and since it involves many factors beyond the physical host, like the network topologies and the routing scheme. Moreover, traffic patterns may vary over time and predicting the resulting effect on the actual available bandwidth between hosts within the data center is extremely difficult. We address this problem by trying to allocate a placement that not only satisfies the predicted communication demand but is also resilient to demand time-variations. This gives rise to a new optimization problem that we call the Min Cut Ratio-aware VM Placement (MCRVMP). The general MCRVMP problem is NP-Hard, hence, we introduce several heuristics to solve it in reasonable time. We present extensive experimental results, associated with both placement computation and run-time performance under time-varying traffic demands, to show that our heuristics provide good results (compared to the optimal solution) for medium size data centers.
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paper_title: QoS-Aware VM Placement in Multi-domain Service Level Agreements Scenarios

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Virtualization technologies of Infrastructure-as-a- Service enable the live migration of running Virtual Machines (VMs) to achieve load balancing, fault-tolerance and hardware consolidation in data centers. However, the downtime/service unavailability due to live migration may be substantial with relevance to the customers' expectations on responsiveness, as the latter are declared in established Service Level Agreements (SLAs). Moreover, it may cause significant (potentially exponential) SLA violation penalties to its associated higher- level domains (Platform-as-a-Service and Software-as-a-Service). Therefore, VM live migration should be managed carefully. In this paper, we present the OpenStack version of the Generic SLA Manager, alongside its strategies for VM selection and allocation during live migration of VMs. We simulate a use case where IaaS (OpenStack-SLAM) and PaaS (OpenShift) are combined, and assess performance and efficiency of the aforementioned VM placement strategies, when a multi-domain SLA pricing & penalty model is involved. We find that our proposal is efficient in managing trade-offs between the operational objectives of service providers (including financial considerations) and the customers' expected QoS requirements.
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paper_title: Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center

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Powerful data centers are the essential supporting infrastructure for mobile, ubiquitous, and cognitive computing, which are the most popular computing paradigms to utilize all kinds of physical resources and provide various services. To ensure the high quality of services, the performance and cost of a data center is a critical factor. In this paper, we investigate the issue of increasing the resource utilization of data centers to improve their performance and lower the cost. It is an efficient way to increase resource utilization via resource sharing. Technically, server virtualization provides the opportunity to share resources in data centers. However, it also introduces other problems, the primary problem being virtual machine placement (VMP), which is to choose a proper physical machine (PM) to deploy virtual machines (VMs) in runtime. We study the virtual machine placement problem with the target of minimizing the total energy consumption by the running of PMs, which is also an indication of resource utilization and the cost of a data center. Due to the multiple dimensionality of physical resources, there always exists a waste of resources, which results from the imbalanced use of multi-dimensional resources. To characterize the multi-dimensional resource usage states of PMs, we present a multi-dimensional space partition model. Based on this model, we then propose a virtual machine placement algorithm EAGLE, which can balance the utilization of multi-dimensional resources, reduce the number of running PMs, and thus lower the energy consumption. We also evaluate our proposed balanced algorithm EAGLE via extensive simulations and experiments on real traces. Experimental results show, over the long run, that EAGLE can save as much as 15% more energy than the first fit algorithm.
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paper_title: Power-Efficient Virtual Machine Placement and Migration in Data Centers

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In this paper, we propose a power-efficient solution for virtual machine placement and migration in a fat tree data center network. This solution reduces power consumption as well as job delay by aggregating virtual machines to a few hyper visors and migrating communicating parties to close locations. In this work, we consider OpenFlow as the implementation protocol. In an OpenFlow environment, a centralized controller oversees job loads, virtual machine requirements and hardware availability. Given observation of such global knowledge, the OpenFlow controller can schedule jobs and distribute virtual machines accordingly. As jobs change and flows shift, the OpenFlow controller dynamically adjusts virtual machine assignments by aggregating virtual machines to close locations in order to save energy. With this placement and migration proposal, more jobs can operate concurrently with close sources and destinations of flows, thus both job and flow delay can be reduced.
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paper_title: Hierarchical Virtual Machine Consolidation in a Cloud Computing System

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Improving the energy efficiency of cloud computing systems has become an important issue because the electric energy bill for 24/7 operation of these systems can be quite large. The focus of this paper is on the virtual machine (VM) consolidation in a cloud computing system as a way of lowering daily energy consumption of the system. In contrast to the existing works that assume resource demands of VMs are known and given as scalar variables, this paper treats these demands as random variables with known means and standard deviations. These random variables may be correlated with one another, and there are several kinds of resources which can be performance bottlenecks. Therefore, both the correlation and multiple resource type should be considered. The VM consolidation problem is then formulated as a multi-capacity stochastic bin packing problem. This problem is NP-hard, so we propose a heuristic method to solve the problem efficiently. The simulation results show that, in spite of its simplicity and scalability, the proposed method produces high quality solutions.
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paper_title: Profiling Applications for Virtual Machine Placement in Clouds

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Application profiling is an important technique for efficient resource management. The decision making of scheduling and resource allocation typically takes great advantage of such a technique primarily for improving resource utilization. With the advent of cloud computing as a multitenant virtualized platform, diverse applications are increasingly deployed onto the cloud and they more than often share physical resources. The background load (other applications running on the same physical machine) is therefore an important factor for profiling an application in this cloud computing scenario. In this paper, we present a novel application profiling technique using the canonical correlation analysis (CCA) method, which identifies the relationship between application performance and resource usage. We further devise a performance prediction model based on application profiles generated using CCA. Clearly, our profiling technique with this prediction model has a lot of potentials particularly in virtual machine (VM) placement with performance awareness. Our experimental results demonstrate the capability of our profiling technique and the accuracy of our prediction model.
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paper_title: Dynamic Energy-Efficient Virtual Machine Placement Optimization for Virtualized Clouds

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A virtual machine placement strategy based on the trade-off between energy consumption and SLA is presented. Aiming at dynamical changes of workload requirements, a self-adaptive placement strategy RLWR based on robust local weight regression is presented, which could decide the overload time of hosts dynamically. After detecting overloaded hosts, one virtual machine migration selection algorithm MNM is proposed. The MNM’s objective is to get minimal migration number. The migrated virtual machines are deployed using bin-packing algorithm PBFDH. The experimental results show that our algorithm has obvious advantages than other algorithms.
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paper_title: Interference-aware virtual machine placement in cloud computing systems

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Cloud computing provides scalable computing and storage resources. These scalable resources can be dynamically organized as many virtual machines (VMs) to run user applications based on a pay-per-use basis. This paper investigates the interference-aware VM placement (IAVMP) problem. In addition to fully exploiting the resources of PMs, the IAVMP problem considers the quality of service (QoS) requirements of user applications and the VM interference reduction. We first formulate the IAVMP problem by an Integer Linear Programming (ILP) model to solve it optimally. Due to the computation complexity of the ILP model, we also propose a polynomial-time heuristic algorithm to efficiently solve the IAVMP problem. Finally, simulation experiments are performed to demonstrate the effectiveness of the proposed heuristic algorithm by comparing with other VM placement algorithms.
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paper_title: Provisioning of requests for virtual machine sets with placement constraints in IaaS clouds

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We study the problem of optimising the provisioning of collections of virtual machines (VMs) having different placement constraints (e.g., security and anti-collocation) and characteristics (e.g., memory and disk capacity), given a set of physical machines (PMs) with known specifications, in order to achieve the objective of maximising an IaaS cloud provider's revenue. We propose two approaches. The first is based on the formulation of the problem as an integer linear programming (ILP) problem, the solution to which provides an optimal VM placement. The second approach is a heuristic based on classifying the requests into different categories and satisfying the constraints in a particular order using a first lit decreasing (FFD) algorithm for multi-dimensional vector bin packing problem. Given a model of VM placement constraints, offered resources and requests with multiple VM types, both approaches devise a placement plan in a way that maximizes revenue, having due regard both to customer requirements and PM capacities. We evaluate the relative performance of the solutions by means of numerical experiments. The results suggest the optimal solution is not practical for medium to large problems, but it is encouraging that the placement plans of the heuristic are close to those of the optimal solution for smaller problem sizes. We use the heuristic to generate results for large scale placement problems; experiments suggest that it is practical in terms of its runtime efficiency and can provide an effective means of online VM-to-PM mapping.
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paper_title: Efficient VM placement with multiple deterministic and stochastic resources in data centers

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Virtual machines (VMs) may significantly improve the efficiency of data center infrastructure by sharing resources of physical servers. This benefit relies on an efficient VM placement scheme to minimize the number of required servers. Existing VM placement algorithms usually assume that VMs' demands for resources are deterministic and stable. However, for certain resources, such as network bandwidth, VMs' demands are bursty and time varying, and demonstrate stochastic nature. In this paper, we study efficient VM placement in data centers with multiple deterministic and stochastic resources. First, we formulate the Multidimensional Stochastic VM Placement (MSVP) problem, with the objective to minimize the number of required servers and at the same time satisfy a predefined resource availability guarantee. Then, we show that the problem is NP-hard, and propose a polynomial time algorithm called Max-Min Multidimensional Stochastic Bin Packing (M3SBP). The basic idea is to maximize the minimum utilization ratio of all the resources of a server, while satisfying the demands of VMs for both deterministic and stochastic resources. Next, we conduct simulations to evaluate the performance of M3SBP. The results demonstrate that M3SBP guarantees the availability requirement for stochastic resources, and M3SBP needs the smallest number of servers to provide the guarantee among the benchmark algorithms.
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paper_title: Data-Aware Virtual Machine Placement and Rate Allocation in Cloud Environment

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Infrastructure-as-a-service cloud provides a suitable environment where cloud users can run data-intensive applications and store their required data files. The performance of data-intensive applications strongly depends on data transmission delay. This delay is a function of the size of data files, the location of virtual machines that run the applications as well as the allocation of data rates to virtual machines. In this paper, we propose a novel virtual machine placement algorithm that jointly optimizes the placement of virtual machines as well as their allocated data rates. Through Simulation results we show that the proposed algorithm can significantly reduce the data transfer delay for virtual machines compared to placement algorithms previously proposed in the literature.
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paper_title: Energy efficient VM placement heuristic algorithms comparison for cloud with multidimensional resources

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Cloud computing provides user utility-oriented IT services, yet accompanied with huge energy consuming, which contributes to the high operational cost as well as CO2 emission. Making Cloud computing energy efficient can lead to a better tradeoff between profit and environmental impact. In this paper, we formulate the energy efficient VM placement problem in Cloud architecture with multidimensional resources and introduce the objective of this problem. Heuristic algorithms including three traditional local search algorithms and generic algorithm (GA) are presented to provide possible optimized solution. We conduct experiments based on Cloudsim. The result shows that GA sometimes provide the best solution, but with poor stabability. Although the BF provide neither the best nor the worst solution most of time, it have the best stabability.
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paper_title: Optimizing VM placement for HPC in the cloud

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Computing as a service model in cloud has encouraged High Performance Computing to reach out to wider scientific and industrial community. Many small and medium scale HPC users are exploring Infrastructure cloud as a possible platform to run their applications. However, there are gaps between the characteristic traits of an HPC application and existing cloud scheduling algorithms. In this paper, we propose an HPC-aware scheduler and implement it atop Open Stack scheduler. In particular, we introduce topology awareness and consideration for homogeneity while allocating VMs. We demonstrate the benefits of these techniques by evaluating them on a cloud setup on Open Cirrus test-bed.
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paper_title: Dynamic optimization of virtual machine placement by resource usage prediction

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In order to save energy with preventing the lack of the resources on servers in data centers, we address the dynamic optimization of virtual machine placement. A decision problem of the virtual machine placement has been formulated as Bin Packing problem. Conventional methods repeat to solve the problem at regular intervals. Live migration is used for changing the virtual machine placement, but some of live migrations are unnecessary; the virtual machine is migrated repeatedly between the physical servers. For the purpose of reducing the unnecessary live migrations, we propose a dynamic optimization of virtual machine placement by resource usage prediction. The proposed method predicts the future resource usage by Auto Regressive Model. The proposed method decides the virtual machine placement by solving the Bin Packing problem with the predicted resource usage.
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paper_title: Server Consolidation Algorithms with Bounded Migration Cost and Performance Guarantees in Cloud Computing

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Consolidation of virtual machines is essential to achieve energy optimization in cloud computing environments. As virtual machines dynamically enter and leave a cloud system, it becomes necessary to relocate virtual machines among servers. However, relocation of virtual machines introduces run-time overheads and consumes extra energy, thus an careful planning for relocation is necessary. We model the relocation problem as a modified bin packing problem and propose a new server consolidation algorithm that guarantees server consolidation with {\em bounded} relocation costs. We also conduct a detailed analysis on the complexity of the server consolidation problem, and give a upper bound on the cost of relocation. Finally, we conduct simulations and compare our server consolidation algorithm with other relocation methods, like First Fit and Best Fit method. The experiment results suggest an interesting trade-off between server consolidation quality and relocation cost. Our algorithm is able to trade about 1\% in server consolidation quality for a reduction about 50\% in relocation cost, when compared with other well known bin packing algorithms. We also note that the relocation cost incurred in our method is much less than the theoretical bound we provided. The reason is that we overestimate the amount of relocation from theoretical analysis, and the actual amount of relocation found from experiments is much less than the worst-case bound from theoretical analysis.
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paper_title: VMPlanner : Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers

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In recent years, the power costs of cloud data centers have become a practical concern and have attracted significant attention from both industry and academia. Most of the early works on data center energy efficiency have focused on the biggest power consumers (i.e., computer servers and cooling systems), yet without taking the networking part into consideration. However, recent studies have revealed that the network elements consume 10-20% of the total power in the data center, which poses a great challenge to effectively reducing network power cost without adversely affecting overall network performance. Based on the analysis on topology characteristics and traffic patterns of data centers, this paper presents a novel approach, called VMPlanner, for network power reduction in the virtualization-based data centers. The basic idea of VMPlanner is to optimize both virtual machine placement and traffic flow routing so as to turn off as many unneeded network elements as possible for power saving. We formulate the optimization problem, analyze its hardness, and solve it by designing VMPlanner as a stepwise optimization approach with three approximation algorithms. VMPlanner is implemented and evaluated in a simulated environment with traffic traces collected from a data center test-bed, and the experiment results illustrate the efficacy and efficiency of this approach.
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paper_title: Workload characteristics-aware virtual machine consolidation algorithms

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An energy conservation strategy must address two issues — placement of virtual machine images and workload characteristics of virtual machines. For performance reason most cloud systems copy a prototype image into the local disk of a physical machine before starting a virtual machine. If the physical machine that stores the image of a virtual machine is off-line, then we cannot run this virtual machine. The workload characteristics of virtual machines determine whether it is data-intensive or CPU-intensive. We assume that the system has a distributed file system therefore a physical machine can run any virtual machine even if it does not have the image. However, we observe that the performance of a data-intensive virtual machine running on a physical machine without its image could result in 60% performance loss compared with running the same virtual machine on a physical machine that has the virtual machine image. On the other hand, the performance of a CPU-intensive virtual machine is almost independent of whether the physical machine has the image or not. As a result, an energy conservation algorithm must consider the workload characteristic of a virtual machine when finding a physical machine to run it, especially for data-intensive virtual machines. This paper proposes a workload characteristics-aware virtual machine consolidation algorithms. We propose an approximation algorithm and two dynamic programmings to consolidate virtual machines and reduce the number of physical machines. We conduct experiments and compare the numbers of physical machines used by our approximation algorithm with the optimal number of physical machines found by our dynamic programming. The experiment results indicate that our approximation algorithm finds good solutions much faster than the dynamic programming.
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paper_title: Multi-objective Virtual Machine Placement with Service Level Agreement: A Memetic Algorithm Approach

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The process of selecting which virtual machines should be located (i.e. executed) at each physical machine of a Datacenter is known as Virtual Machine Placement VMP. This work proposes for the first time a multi-objective formulation of the VMP considering Service Level Agreement. A novel multiobjective memetic algorithm is also proposed to solve the formulated multi-objective problem. This proposal is validated comparing experimental results of the proposed algorithm with a brute force exhaustive search algorithm. Simulations prove the correctness of the proposed memetic algorithm and its scalability considering different experimental scenarios.
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paper_title: Improved energy-efficiency in cloud datacenters with interference-aware virtual machine placement

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Virtualization is one of the main technologies used for improving resource efficiency in datacenters; it allows the deployment of co-existing computing environments over the same hardware infrastructure. However, the co-existing of environments — along with management inefficiencies — often creates scenarios of high-competition for resources between running workloads, leading to performance degradation. This phenomenon is known as Performance Interference, and introduces a non-negligible overhead that affects both a datacenter's Quality of Service and its energy-efficiency. This paper introduces a novel approach to workload allocation that improves energy-efficiency in Cloud datacenters by taking into account their workload heterogeneity. We analyze the impact of performance interference on energy-efficiency using workload characteristics identified from a real Cloud environment, and develop a model that implements various decision-making techniques intelligently to select the best workload host according to its internal interference level. Our experimental results show reductions in interference by 27.5% and increased energy-efficiency up to 15% in contrast to current mechanisms for workload allocation.
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paper_title: Guaranteeing High Availability Goals for Virtual Machine Placement

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The placement of virtual machines (VMs) on a cluster of hosts under multiple constraints, including administrative (security, regulations) resource-oriented (capacity, energy), and QoS-oriented (performance) is a highly complex task. We define a new high-availability property for a VM, when a VM is marked as k-resilient, as long as there are up to k host failures, it should be guaranteed that it can be relocated to a non-failed host without relocating other VMs. Together with Hardware Predictive Failure Analysis and live migration, which enable VMs to be evacuated from a host before it fails, this property allows the continuous running of VMs on the cluster despite host failures. The complexity of the constraints associated with k-resiliency, which are naturally expressed by Second Order logic statements, prevented their integration into the placement computation until now. We present a novel algorithm which enables this integration by transforming the k-resiliency constraints to rules consumable by a generic Constraint Programming engine, prove that it guarantees the required resiliency and describe the implementation. We provide some preliminary results and compare our high availability support with naive solutions.
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paper_title: Particle Swarm Optimization for Energy-Aware Virtual Machine Placement Optimization in Virtualized Data Centers

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A critical research issue is to lower the energy consumption of a virtualized data center by means of virtual machine placement optimization while satisfying the resource requirements of the cloud services. In this paper, we focus on different existing schemes and on the energy-aware virtual machine placement optimization problem of a heterogeneous virtualized data center. We attempt to explore a better alternative approach to minimizing the energy consumption, and we observe that particle swarm optimization (PSO) has considerable potential. However, the PSO must be improved to solve an optimization problem. The improvement includes redefining the parameters and operators of the PSO, adopting an energy-aware local fitness first strategy and designing a novel coding scheme. Using the improved PSO, an optimal virtual machine replacement scheme with the lowest energy consumption can be found. Experimental results indicate that our approach significantly outperforms other approaches, and can lessen 13%-23% energy consumption in the context of this paper.
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paper_title: Intelligent virtual machine placement for cost efficiency in geo-distributed cloud systems

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An important challenge of running large-scale cloud services in a geo-distributed cloud system is to minimize the overall operating cost. The operating cost of such a system includes two major components: electricity cost and wide-area-network (WAN) communication cost. While the WAN communication cost is minimized when all virtual machines (VMs) are placed in one datacenter, the high workload at one location requires extra power for cooling facility and results in worse power usage effectiveness (PUE). In this paper, we develop a model to capture the intrinsic trade-off between electricity and WAN communication costs, and formulate the optimal VM placement problem, which is NP-hard due to its binary and quadratic nature. While exhaustive search is not feasible for large-scale scenarios, heuristics which only minimize one of the two cost terms yield less optimized results. We propose a cost-aware two-phase metaheuristic algorithm, Cut-and-Search, that approximates the best trade-off point between the two cost terms. We evaluate Cut-and-Search by simulating it over multiple cloud service patterns. The results show that the operating cost has great potential of improvement via optimal VM placement. Cut-and-Search achieves a highly optimized trade-off point within reasonable computation time, and outperforms random placement by 50%, and the partial-optimizing heuristics by 10-20%.
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paper_title: Energy-aware virtual machine placement in data centers

paper_content:

The energy efficiency of modern data centers has become a practical concern and has attracted significant attention in recent years. In contract to existing solutions that primarily focuses on only one specific aspect of management to reduce energy consumption, this paper explores the balance between server energy consumption and network energy consumption to present an energy-aware joint virtual machine (VM) placement. Given the definition of VM placement fairness, the basic algorithm of VM placement which fulfills server energy consumption constraints is conducted. Then, we further formulate the VM placement as an optimization problem which considers application dependencies to reduce network energy consumption. We design a joint algorithm that efficiently solves the VM placement problem for very large problem sizes. Using simulations, we conduct a comparative analysis on the impact of the data center architectures, server constraints and application dependencies on the potential performance gain of energy-aware VM placement. Compared to existing generic methods, we show a significant performance improvement such as efficiently reducing the number of physical machines used to save server energy consumption, decreasing the communication distance between VMs to obtain data center network energy consumption efficiency, improving scalability of data centers.
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paper_title: A network-aware virtual machine placement algorithm in mobile cloud computing environment

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By taking into consideration network features of a wireless mesh network environment where mobile cloud computing (MCC) system is deployed, this paper proposes an effective virtual machine (VM) placement algorithm. This algorithm tends to place a newly created VM for a cloud service to a cloud server, which itself is also a mobile device, that occurs lowest network delay when accessing to application data stored in wireline networks. The aim is to reduce cloud service response time as much as possible in order to deliver better quality of experience to cloud end users over more noisy and less robust wireless networks. In addition, the proposed deployment of cloud storage and cloud computational resources onto wireless networks is also a contribution of the paper. The simulation results have shown the effectiveness and the efficiency of the proposed VM placement algorithms both in terms of cloud service response time.
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paper_title: A Hybrid Genetic Algorithm for the Energy-Efficient Virtual Machine Placement Problem in Data Centers

paper_content:

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation technology. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches consider the energy consumption by physical machines only, but do not consider the energy consumption in communication network, in a data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement. In our preliminary research, we have proposed a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both physical machines and the communication network in a data center. Aiming at improving the performance and efficiency of the genetic algorithm, this paper presents a hybrid genetic algorithm for the energy-efficient virtual machine placement problem. Experimental results show that the hybrid genetic algorithm significantly outperforms the original genetic algorithm, and that the hybrid genetic algorithm is scalable.
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paper_title: Migration-based virtual machine placement in cloud systems

paper_content:

Cloud computing is an emerging technology that greatly shapes our lives, where users run jobs on virtual machines (VMs) on physical machines (PMs) provided by a cloud provider, saving the investment in upfront infrastructures. Due to the heterogeneity of various jobs, different VMs on the same PM can have different job completion times. Meanwhile, the PMs are also heterogeneous. Therefore, different VM placements have different job completion times. Our objective is to minimize the total job completion time of the input VM requests through a reasonable VM placement schedule. This problem is NP-hard, since it can be reduced to a knapsack problem. We propose an off-line VM placement method through emulated VM migration, while the on-line VM placement is solved by a real VM migration process. The migration algorithm is a heuristic approach, where we place the VM to its best PM directly, as long as it has enough capacity. Otherwise, if the migration constraint is satisfied, we migrate another VM from this PM to accommodate the new VM. Furthermore, we study a hybrid scheme where a batch is employed to accept upcoming VMs for the on-line scenario. Evaluation results prove the high efficiency of our algorithms.
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paper_title: An Energy Efficient Virtual Machine Placement Algorithm with Balanced Resource Utilization

paper_content:

Virtualization is a key technology for resource sharing in IaaS/PaaS cloud infrastructures. One primary issue in virtualization is the virtual machine placement (VMP) problem, which is to choose proper physical machine (PM) to deploy virtual machines (VMs) in runtime. In this paper, we study the VMP problem with the goal of minimizing the total energy consumption. We first present a multi-dimensional space partition model to characterize the resource usage states of PMs. Based on this model, we then propose a virtual machine placement algorithm, which can balance the utilization of multi-dimensional resources, reduce the number of running PMs and thus lower down the energy consumption. We also evaluate our proposed balanced algorithm via extensive simulations. Simulation results show that our approach can save as much as 15% energy compared to the first fit algorithm over a long run.
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paper_title: Reducing electricity cost through virtual machine placement in high performance computing clouds

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In this paper, we first study the impact of load placement policies on cooling and maximum data center temperatures in cloud service providers that operate multiple geographically distributed data centers. Based on this study, we then propose dynamic load distribution policies that consider all electricity-related costs as well as transient cooling effects. Our evaluation studies the ability of different cooling strategies to handle load spikes, compares the behaviors of our dynamic cost-aware policies to cost-unaware and static policies, and explores the effects of many parameter settings. Among other interesting results, we demonstrate that (1) our policies can provide large cost savings, (2) load migration enables savings in many scenarios, and (3) all electricity-related costs must be considered at the same time for higher and consistent cost savings.
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paper_title: An energy-efficient approach for virtual machine placement in cloud based data centers

paper_content:

Cloud computing is a new technology which is proffering IT services based on pay-as-you-go model to consumers from everywhere in the world. The growing demand of Cloud infrastructure and modern computational requests like business, scientific and web applications result in large-scale data centers and lead to extra electrical energy consumption. High energy consumption causes high operational cost and also led to high carbon emission which is harmful for atmosphere. Hence, energy-efficient techniques are required to minimize the negative effects of Cloud computing on the environment. Virtual machines (VMs) migration, dynamic consolidation in the virtualized data centers in cloud environments and switching idle physical machines off could yield reduce energy consumption; hence, one of the important issues for energy efficiency in virtualized cloud environments is how to place new VMs or selected VMs for migration across the hosts. In this paper we propose an energy-efficient approach based on Minimum Correlation Coefficient (MCC) method for virtual machine placement in cloud based, virtualized data centers. The proposed approach regards both Service Level Agreement (SLA) and low energy consumption and tries to make a trade-off between these two concerns using fuzzy Analytic Hierarchy Process (AHP). We evaluate our approach using Cloudsim toolkit as a modern cloud computing environment simulator. The evaluation shows that our proposed method offers a suitable trade-off between power efficiency and SLA violation reduction in cloud data centers.
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paper_title: Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement

paper_content:

The scalability of modern data centers has become a practical concern and has attracted significant attention in recent years. In contrast to existing solutions that require changes in the network architecture and the routing protocols, this paper proposes using traffic-aware virtual machine (VM) placement to improve the network scalability. By optimizing the placement of VMs on host machines, traffic patterns among VMs can be better aligned with the communication distance between them, e.g. VMs with large mutual bandwidth usage are assigned to host machines in close proximity. We formulate the VM placement as an optimization problem and prove its hardness. We design a two-tier approximate algorithm that efficiently solves the VM placement problem for very large problem sizes. Given the significant difference in the traffic patterns seen in current data centers and the structural differences of the recently proposed data center architectures, we further conduct a comparative analysis on the impact of the traffic patterns and the network architectures on the potential performance gain of traffic-aware VM placement. We use traffic traces collected from production data centers to evaluate our proposed VM placement algorithm, and we show a significant performance improvement compared to existing general methods that do not take advantage of traffic patterns and data center network characteristics.
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paper_title: Joint VM placement and routing for data center traffic engineering

paper_content:

Today's data centers need efficient traffic management to improve resource utilization in their networks. In this work, we study a joint tenant (e.g., server or virtual machine) placement and routing problem to minimize traffic costs. These two complementary degrees of freedom—placement and routing—are mutually-dependent, however, are often optimized separately in today's data centers. Leveraging and expanding the technique of Markov approximation, we propose an efficient online algorithm in a dynamic environment under changing traffic loads. The algorithm requires a very small number of virtual machine migrations and is easy to implement in practice. Performance evaluation that employs the real data center traffic traces under a spectrum of elephant and mice flows, demonstrates a consistent and significant improvement over the benchmark achieved by common heuristics.
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paper_title: Definition of Security Metrics for the Cloud Computing and Security-Aware Virtual Machine Placement Algorithms

paper_content:

Nowadays, Cloud Computing is becoming a key factor in computer science. Besides the great benefits it brought to the information technology and to the economy, Cloud Computing shows some weakness when looking at the security. An IaaS client should be able to specify its security requirements. But the lack of a system of security metrics leads to the incapability of quantifying the security level of a client deployment in a Cloud. Therefore, we propose a system of security metrics specific to the Cloud Computing and use it to develop virtual machines placement algorithms.
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paper_title: VMPlanner : Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers

paper_content:

In recent years, the power costs of cloud data centers have become a practical concern and have attracted significant attention from both industry and academia. Most of the early works on data center energy efficiency have focused on the biggest power consumers (i.e., computer servers and cooling systems), yet without taking the networking part into consideration. However, recent studies have revealed that the network elements consume 10-20% of the total power in the data center, which poses a great challenge to effectively reducing network power cost without adversely affecting overall network performance. Based on the analysis on topology characteristics and traffic patterns of data centers, this paper presents a novel approach, called VMPlanner, for network power reduction in the virtualization-based data centers. The basic idea of VMPlanner is to optimize both virtual machine placement and traffic flow routing so as to turn off as many unneeded network elements as possible for power saving. We formulate the optimization problem, analyze its hardness, and solve it by designing VMPlanner as a stepwise optimization approach with three approximation algorithms. VMPlanner is implemented and evaluated in a simulated environment with traffic traces collected from a data center test-bed, and the experiment results illustrate the efficacy and efficiency of this approach.
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paper_title: On Theory of VM Placement: Anomalies in Existing Methodologies and Their Mitigation Using a Novel Vector Based Approach

paper_content:

In this paper, we present the methodologies used in existing literature for Virtual Machine (VM) placement, load balancing and server consolidation in a data center environment. While the methodologies may seem fine on the surface, certain drawbacks and anomalies can be uncovered when they are analyzed deeper. We point out those anomalies and draw backs in the existing literature and explain what are the root causes of such anomalies. Then we propose a novel methodology based on vector arithmetic which not only addresses those anomalies but also leads to some interesting theories and algorithms to tackle the above mentioned three functionalities required in managing resources of data centers. We believe that with a strong mathematical base, our methodology has the potential to become the foundation of future models and algorithms in this research area.
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paper_title: Towards Profitable Virtual Machine Placement in the Data Center

paper_content:

Motivated by the limit on the power usage effectiveness (PUE) of the data centers, the potential benefit of the consolidation, and the impetus of achieving maximum return on investment (ROI) on the cloud computing market, we investigate VM placement in the data center, formulate a multi-level generalized assignment problem (MGAP) for maximizing the profit under the service level agreement and the power budget constraint based on the model of a virtualized data center, and solve it with a first-fit heuristic. Numerical simulations show that the first-fit heuristic is effective in solving the large-scale instances of the MGAP with the sampled simulation setups.
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paper_title: Multiobjective Virtual Machine Placement in Cloud Environment

paper_content:

In this paper, the virtual machine placement problem is formulated as a multi-objective optimization problem. The objectives are maximizing profit, maximizing load balancing and minimizing recourse wastage. Results of Genetic algorithms, Non-dominated Sorting Genetic Algorithm and Non-dominated Sorting Genetic Algorithm-II are compared with common solution representation, penalty and benefit values. All the three algorithms reported good solutions whereas GA and NSGA are subjected to premature convergence and duplicate solutions. NSGA-II gives good and diversified range of solutions.
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paper_title: SLA guaranteed virtual machine consolidation for computing clouds

paper_content:

One of the most attractive features that computing clouds can offer is that the data center maintenance cost can be reduced by consolidating the workload generated by the virtual machines (VMs) into a few powerful physical servers. In this paper, the problem of maximally consolidating heterogeneous virtual machines into servers while protecting the service level agreement of each virtual machine is investigated. A generic and robust VM workload consolidation framework is proposed. The proposed framework achieves several attractive properties like low maintenance cost, low algorithm complexity, adjustable resource provisioning aggressiveness, and good scalability.
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paper_title: Prevent VM Migration in Virtualized Clusters via Deadline Driven Placement Policy

paper_content:

VM consolidation has been shown as a promising technique for saving energy costs of a data center. It relies on VM migration to move user applications or jobs onto fewer numbers of physical servers during off peak hour. However, VM migration is a costly operation that could cause several concerns, such as performance degradation and system instability. Most existing works were proposed to minimize the migration cost for dynamic consolidation which migrates VM at the runtime when SLA violation or resource under-utilization is detected. In contrast, this paper aims to proactively prevent VM migration for semi-static VM consolidation by proposing a deadline driven VM placement strategy based on the awareness of the server turn-off time and job execution time. We evaluate our approach using a real HPC cluster trace as well as a set of synthetic generated workloads. The results show our approach can significantly reduce the number of migrations by 70% on the real trace. We also demonstrate that our approach can be resilient to different workload patterns by achieving consistent improvement around 50% over all the synthetic workloads.
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paper_title: A Two Stage Heuristic Algorithm for Solving the Server Consolidation Problem with Item-Item and Bin-Item Incompatibility Constraints

paper_content:

The problem of server sprawl is common in data centers of most business organizations. It is most often the case that an application is run on dedicated servers. This leads to situations where organizations end up having numerous servers that remain under-utilized most of the times. The servers, in such scenarios, are allocated more resources (disk, cpu and memory) than are justified by their present workloads. Consolidating multiple underutilized servers into a fewer number of non-dedicated servers that can host multiple applications is an effective tool for businesses to enhance their returns on investment. The problem can be modeled as a variant of the bin packing problem where items to be packed are the servers being consolidated and bins are the target servers. The sizes of the servers/items being packed are resource utilizations which are obtained from the performance trace data. Here we describe a novel two stage heuristic algorithm for taking care of the "bin-item" incompatibility constraints that are inherent in any server consolidation problem. The model is able to solve extremely large instances of problem in a reasonable amount of time.
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paper_title: Virtual machine placement for predictable and time-constrained peak loads

paper_content:

We present an approach to optimal virtual machine placement within datacenters for predicable and time-constrained load peaks. A method for optimal load balancing is developed, based on binary integer programming. For tradeoffs between quality of solution and computation time, we also introduce methods to pre-process the optimization problem before solving it. Upper bound based optimizations are used to reduce the time required to compute a final solution, enabling larger problems to be solved. For further scalability, we also present three approximation algorithms, based on heuristics and/or greedy formulations. The proposed algorithms are evaluated through simulations based on synthetic data sets. The evaluation suggests that our algorithms are feasible, and that these can be combined to achieve desired tradeoffs between quality of solution and execution time.
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paper_title: On Modelling Virtual Machine Consolidation to Pseudo-Boolean Constraints

paper_content:

Cloud Computing is a new paradigm of distributed computing that offers virtualized resources and services over the Internet. To offer Infrastructure-as-a-Service (IaaS) many Cloud providers uses a large data center which usage ranges 5% to 10% of capacity in average. In order to improve Cloud data center management and resources usage a Virtual Machine (VM) consolidation technique can be applied to increase workloads and save energy. Using VM consolidation, we introduce an artificial intelligence consolidation based in Pseudo-Boolean (PB) Constraints to find a optimal consolidation. To evaluate our PB consolidation approach we used the DInf-UFPR and Google Cluster scenario and the formulas are solved with two state-of-the-art solvers.
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paper_title: Time-Aware VM-Placement and Routing with Bandwidth Guarantees in Green Cloud Data Centers

paper_content:

Variation in network performance due to the shared resources is a key obstacle for cloud adoption. Thus, the success of cloud providers to attract more tenants depends on their ability to provide bandwidth guarantees. Power efficiency in data centers has become critically important for supporting larger number of tenants. In this paper, we address the problem of time-aware VM-placement and routing (TVPR), where each tenant requests for a specified amount of server resources (VMs) and network resource (bandwidth) for a given duration. The TVPR problem allocates the required resources for as many tenants as possible by finding the right set of servers to map their VMs and routing their traffic so as to minimize the total power consumption. We propose a multi-component utilization-based power model to determine the total power consumption of a data center according to the resource utilization of the components (servers and switches). We then develop a mixed integer linear programming (MILP) optimization problem formulation based on the proposed power model and prove it to be N P-complete. Since the TVPR problem is computationally prohibitive, we develop a fast and scalable heuristic algorithm. To demonstrate the efficiency of our proposed algorithm, we compare its performance with the numerical results obtained by solving the MILP problem using CPLEX, for a small data center. We then demonstrate the effectiveness of the proposed algorithm in terms of power consumption and acceptance ratio for large data centers through simulation results.
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paper_title: Online traffic-aware virtual machine placement in data center networks

paper_content:

Data centers rely on virtualization to provide different services over a shared infrastructure. The placement of the different services and tasks in the physical machines is crucial for the performance of the whole system. A misplaced service can overload some network links, lead to congestion, or even connection disruptions. On the other hand, virtual machine migration allows reallocating services and changing the traffic matrix, leading to more efficient use of bandwidth. In this paper, we propose a Virtual Machine Placement (VMP) algorithm to (re)allocate virtual machines in the data center servers, based on the current traffic matrix, CPU, and memory usage. Analyzing the formation of community patterns in terms of traffic using graph theory, we are able to find virtual machines that are correlated because they exchange high amount of data. Those virtual machines are aggregated and allocated to servers as close as possible to each other, reducing traffic congestion. Our simulation results show that VMP was able to improve the traffic distribution. In some specific cases we were able to reduce 80% of the core traffic, concentrating it at the edge of the network.
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paper_title: A simulated annealing algorithm for energy efficient virtual machine placement

paper_content:

Improving energy efficiency has become increasingly important in data centers in recent years to reduce the rapidly growing tremendous amounts of electricity consumption. The power dissipation of the physical servers is the root cause of power usage of other systems, such as cooling systems. Many efforts have been made to make data centers more energy efficient. One of them is to minimize the total power consumption of these servers in a data center through virtual machine consolidation, which is implemented by virtual machine placement. The placement problem is often modeled as a bin packing problem. Due to the NP-hard nature of the problem, heuristic solutions such as First Fit and Best Fit algorithms have been often used and have generally good results. However, their performance leaves room for further improvement. In this paper we propose a Simulated Annealing based algorithm, which aims at further improvement from any feasible placement. This is the first published attempt of using SA to solve the VM placement problem to optimize the power consumption. Experimental results show that this SA algorithm can generate better results, saving up to 25 percentage more energy than First Fit Decreasing in an acceptable time frame.
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paper_title: Virtual Machine Placement Optimization Supporting Performance SLAs

paper_content:

Cloud computing model separates usage from ownership in terms of control on resource provisioning. Resources in the cloud are projected as a service and are realized using various service models like IaaS, PaaS and SaaS. In IaaS model, end users get to use a VM whose capacity they can specify but not the placement on a specific host or with which other VMs it can be co-hosted. Typically, the placement decisions happen based on the goals like minimizing the number of physical hosts to support a given set of VMs by satisfying each VMs capacity requirement. However, the role of the VMM usage to support I/O specific workloads inside a VM can make this capacity requirement incomplete. I/O workloads inside VMs require substantial VMM CPU cycles to support their performance. As a result, placement algorithms need to include the VMM's usage on a per VM basis. Secondly, cloud centers encounter situations wherein change in existing VM's capacity or launching of new VMs need to be considered during different placement intervals. Usually, this change is handled by migrating existing VMs to meet the goal of optimal placement. We argue that VM migration is not a trivial task and does include loss of performance during migration. We quantify this migration overhead based on the VM's workload type and include the same in placement problem. One of the goals of the placement algorithm is to reduce the VM's migration prospects, thereby reducing chances of performance loss during migration. This paper evaluates the existing ILP and First Fit Decreasing (FFD) algorithms to consider these constraints to arrive at placement decisions. We observe that ILP algorithm yields optimal results but needs long computing time even with parallel version. However, FFD heuristics are much faster and scalable algorithms that generate a sub-optimal solution, as compared to ILP, but in time-scales that are useful in real-time decision making. We also observe that including VM migration overheads in the placement algorithm results in a marginal increase in the number of physical hosts but a significant, of about 84 percent reduction in VM migration.
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paper_title: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing

paper_content:

Virtual machine placement is a process of mapping virtual machines to physical machines. The optimal placement is important for improving power efficiency and resource utilization in a cloud computing environment. In this paper, we propose a multi-objective ant colony system algorithm for the virtual machine placement problem. The goal is to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption. The proposed algorithm is tested with some instances from the literature. Its solution performance is compared to that of an existing multi-objective genetic algorithm and two single-objective algorithms, a well-known bin-packing algorithm and a max-min ant system (MMAS) algorithm. The results show that the proposed algorithm is more efficient and effective than the methods we compared it to.
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paper_title: Design and Implementation of a Multi-objective Optimization Mechanism for Virtual Machine Placement in Cloud Computing Data Center

paper_content:

Cloud computing is becoming a popular way of supplying and using computing resources. A cloud-computing data center is equipped with a large number of physical resources and must manage an even larger number of virtual machines (VMs). The center’s VM placement strategy affects the utilization of physical resources, and consequently, it influences operational costs. Our goal is to develop a multi-objective optimization mechanism for VM placement that satisfies various constraints and results in the lowest operational cost. The number of possible combinations of VMs and hosts can be extremely large. For the mechanism to be practical, the number of possible combinations must be reduced. We reduced computational overheads by classifying VM hosts into a relatively small number of equivalent sets. Simulation results show that expected operational costs can be significantly reduced by applying the proposed mechanism.
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paper_title: An Availability-Aware Virtual Machine Placement Approach for Dynamic Scaling of Cloud Applications

paper_content:

Cloud computing promises customers the on-demand ability to dynamically provision virtualization resources in face of workload variations. Most existing scaling approaches addressed this problem by allocating application to a certain amount of cloud resources. However, the problem of the availability of application influenced by VM-based physical locations during resource scaling process is a serious challenge due to dynamic complex workload and has not been widely discussed yet. In this paper, we present a novel availability-based computing model to describe availability attribute of one application in the hierarchical structured cloud. Moreover, we propose an availability-aware policy by performing both vertical and horizontal scaling to explore how and where to allocate computing resource. Simulation results indicate that our model captured the availability of cloud applications properly and proposed scaling approach achieves the objectives of meeting availability demands and minimizing the total communication cost.
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paper_title: Improving Performance and Availability of Services Hosted on IaaS Clouds with Structural Constraint-Aware Virtual Machine Placement

paper_content:

The increasing popularity of modern virtualization-based datacenters continues to motivate both industry and academia to provide answers to a large variety of new and challenging questions. In this paper we aim to answer focusing on one such question: how to improve performance and availability of services hosted on IaaS clouds. Our system, structural constraint-aware virtual machine placement (SCAVP), supports three types of constraints: demand, communication and availability. We formulate SCAVP as an optimization problem and show its hardness. We design a hierarchical placement approach with four approximation algorithms that efficiently solves the SCAVP problem for large problem sizes. We provide a formal model for the application (to better understand structural constraints) and the datacenter (to effectively capture capabilities), and use the two models as inputs to the placement problem. We evaluate SCAVP in a simulated environment to illustrate the efficiency and importance of the proposed approach.
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paper_title: An energy-aware heuristic framework for virtual machine consolidation in Cloud computing

paper_content:

Virtual machine (VM) consolidation in Cloud computing provides a great opportunity for energy saving. However, the obligation of providing suitable quality of service to end users leads to the necessity in dealing with energy-performance tradeoff. In this paper, we propose a redesigned energy-aware heuristic framework for VM consolidation to achieve a better energy-performance tradeoff. There are two main contributions in the framework: (1) establish a service level agreement (SLA) violation decision algorithm to decide whether a host is overload with SLA violation; (2) minimum power and maximum utilization policy is then proposed to improve the Minimum Power policy in previous work. Finally, we have evaluated our framework through simulation on large-scale experiments driven by workload traces from more than a thousand VMs, and the results show that our framework outperforms previous work. Specifically, it guarantees 21---34 % decrease in energy consumption, 84---92 % decrease in SLA violation, 87---94 % decrease in energy-performance metric, and 63 % decrease in execution time. And we further discuss why the redesigned framework outperforms the previous design.
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paper_title: Optimal virtual machine placement across multiple cloud providers

paper_content:

Cloud computing provides users an efficient way to dynamically allocate computing resources to meet demands. Cloud providers can offer users two payment plans, i.e., reservation and on-demand plans for resource provisioning. Price of resources in reservation plan is generally cheaper than that in on-demand plan. However, since the reservation plan has to be acquired in advance, it may not fully meet future demands in which the on-demand plan can be used to guarantee the availability to the user. In this paper, we propose an optimal virtual machine placement (OVMP) algorithm. This algorithm can minimize the cost spending in each plan for hosting virtual machines in a multiple cloud provider environment under future demand and price uncertainty. OVMP algorithm makes a decision based on the optimal solution of stochastic integer programming (SIP) to rent resources from cloud providers. The performance of OVMP algorithm is evaluated by numerical studies and simulation. The results clearly show that the proposed OVMP algorithm can minimize users' budgets. This algorithm can be applied to provision resources in emerging cloud computing environments.
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paper_title: VM Placement Strategies for Cloud Scenarios

paper_content:

The problem of Virtual Machine (VM) placement in a compute cloud infrastructure is well-studied in the literature. However, the majority of the existing works ignore the dynamic nature of the incoming stream of VM deployment requests that continuously arrive to the cloud provider infrastructure. In this paper we provide a practical model of cloud placement management under a stream of requests and present a novel technique called Backward Speculative Placement (BSP)that projects the past demand behavior of a VM to a candidate target host. We exploit the BSP technique in two algorithms, first for handling the stream of deployment requests, second in a periodic optimization, to handle the dynamic aspects of the demands. We show the benefits of our BSPtechnique by comparing the results on a simulation period with a strategy of choosing an optimal placement at each time instant, produced by a generic MIP solver.
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paper_title: Redundant virtual machine placement for fault-tolerant consolidated server clusters

paper_content:

Consolidated server systems using server virtualization involves serious risks of host server failures that induce unexpected downs of all hosted virtual machines and applications. To protect applications requiring high-availability from unpredictable host server failures, redundant configuration using virtual machines can be an effective countermeasure. This paper presents a virtual machine placement method for establishing a redundant configuration against host server failures with less host servers. The proposed method estimates the requisite minimum number of virtual machines according to the performance requirements of application services and decides an optimum virtual machine placement so that minimum configurations survive at any k host server failures. The evaluation results clarify that the proposed method achieves requested fault-tolerance level with less number of hosting servers compared to the conventional N+M redundant configuration approach.
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paper_title: Elasticity-aware virtual machine placement for cloud datacenters

paper_content:

With the increasing popularity of cloud computing, the cloud datacenter suffers from both limited resources and the variation of users' requests. One important feature of cloud computing is on-demand scaling, enabling the fluctuation of one user's resource demand. However, amongst previous work concerning the virtual machine (VM) placement in datacenters, satisfying the VMs' requested resources of users is the primary objective, neglecting future demand variation. In this paper, we propose the concept of elasticity, referring to how well the datacenter can satisfy the growth of the input VMs resource demands under both the limitations of physical machines (PMs) capacities and links capacities. To consider both dimensions of the machine and bandwidth resources simultaneously, we propose our hierarchical VM placement algorithm. We also prove the optimality of our algorithm in a frequently used semi-homogeneous datacenter configuration. Furthermore, we study the heterogeneous datacenter configuration, favoring the characteristics of multi-tenant datacenters. Evaluation results validate the efficiency of our algorithm.
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paper_title: A Matrix Transformation Algorithm for Virtual Machine Placement in Cloud

paper_content:

The virtual machine (VM) placement is an important problem of cloud data center resource management. There have been many research efforts to address this problem. However, existing research is mainly concerned with single objective such as the number of the physical machine (PM) used, and few research consider the resource utilization rate of PM's different dimensions. In this paper, we present a two-objective optimization model such that the number of required PM is minimized and the resource utilization of occupied PM is optimum. We further provide an algorithm based on matrix transformation. In the algorithm, we define cluster matrix of VM request sequence and VM placement initial matrix to generate the optimum solution of VM placement optimization model. Finally, we compare our algorithm with the other VM placement strategy, the experimental results demonstrate the good performance of our algorithm.
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paper_title: Virtual Machine Placement for Improving Energy Efficiency and Network Performance in IaaS Cloud

paper_content:

Under the premise of ensuring application performance, how to place virtual machines (VMs) on physical machines (PMs) to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers in IaaS cloud. The existing VM placement schemes are mostly to reduce energy consumption by optimizing utilization of physical server or network elements, but the issue of aggressive consolidation of VM is ignored, which may lead to network performance degradation. To address the issue, this paper proposes a VM placement scheme based on a new two-stage heuristic algorithm to optimize network performance and to reduce energy consumption of physical servers and network elements, so as to finally achieve the tradeoff between energy efficiency and network performance. The simulations show that our solution achieves good results.
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paper_title: Energy-Saving Virtual Machine Placement in Cloud Data Centers

paper_content:

In cloud data centers, different mapping relationships between virtual machines (VMs) and physical machines (PMs) cause different resource utilization, therefore, how to place VMs on PMs to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers. The existing VM placement schemes are to optimize physical server resources utilization or network resources utilization, but few of them focuses on optimizing multiple resources utilization simultaneously. To address the issue, this paper proposes a VM placement scheme meeting multiple resource constraints, such as the physical server size (CPU, memory, storage, bandwidth, etc.) and network link capacity to improve resource utilization and reduce both the number of active physical servers and network elements so as to finally reduce energy consumption. Since VM placement problem is abstracted as a combination of bin packing problem and quadratic assignment problem, which is also known as a classic combinatorial optimization and NP-hard problem, we design a novel greedy algorithm by combining minimum cut with the best-fit, and the simulations show that our solution achieves better results.
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paper_title: Higher SLA satisfaction in datacenters with continuous VM placement constraints

paper_content:

In a virtualized datacenter, the Service Level Agreement for an application restricts the Virtual Machines (VMs) placement. An algorithm is in charge of maintaining a placement compatible with the stated constraints. Conventionally, when a placement algorithm computes a schedule of actions to re-arrange the VMs, the constraints ignore the intermediate states of the datacenter to only restrict the resulting placement. This situation may lead to temporary violations of the constraints. In this paper, we discuss the causes of these violations. We then advocate for continuous placement constraints to restrict also the actions schedule. We discuss why their development requires more attention and how the extensible placement algorithm BtrPlace can address this issue.
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paper_title: Energy-Efficient Virtual Machine Placement in Data Centers by Genetic Algorithm

paper_content:

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches to the virtual machine placement problem consider the energy consumption by physical machines in a data center only, but do not consider the energy consumption in communication network in the data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement in order to make the data center more energy-efficient. In this paper, we propose a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both the servers and the communication network in the data center. Experimental results show that the genetic algorithm performs well when tackling test problems of different kinds, and scales up well when the problem size increases.
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paper_title: Shadow-routing based dynamic algorithms for virtual machine placement in a network cloud

paper_content:

We consider a shadow routing based approach to the problem of real-time adaptive placement of virtual machines (VM) in large data centers (DC) within a network cloud. Such placement in particular has to respect vector packing constraints on the allocation of VMs to host physical machines (PM) within a DC, because each PM can potentially serve multiple VMs simultaneously. Shadow routing is attractive in that it allows a large variety of system objectives and/or constraints to be treated within a common framework (as long as the underlying optimization problem is convex). Perhaps even more attractive feature is that the corresponding algorithm is very simple to implement, it runs continuously, and adapts automatically to changes in the VM demand rates, changes in system parameters, etc., without the need to re-solve the underlying optimization problem “from scratch”. In this paper we focus on the minmax-DC-load problem. Namely, we propose a combined VM-toDC routing and VM-to-PM assignment algorithm, referred to as Shadow scheme, which minimizes the maximum of appropriately defined DC utilizations. We prove that the Shadow scheme is asymptotically optimal (as one of its parameters goes to 0). Simulation confirms good performance and high adaptivity of the algorithm. Favorable performance is also demonstrated in comparison with a baseline algorithm based on VMware implementation [7], [8]. We also propose a simplified - “more distributed” - version of the Shadow scheme, which performs almost as well in simulations.
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paper_title: Optimizing data access latencies in cloud systems by intelligent virtual machine placement

paper_content:

Many cloud applications are data intensive requiring the processing of large data sets and the MapReduce/Hadoop architecture has become the de facto processing framework for these applications. Large data sets are stored in data nodes in the cloud which are typically SAN or NAS devices. Cloud applications process these data sets using a large number of application virtual machines (VMs), with the total completion time being an important performance metric. There are many factors that affect the total completion time of the processing task such as the load on the individual servers, the task scheduling mechanism, communication and data access bottlenecks, etc. One dominating factor that affects completion times for data intensive applications is the access latencies from processing nodes to data nodes. Ideally, one would like to keep all data access local to minimize access latency but this is often not possible due to the size of the data sets, capacity constraints in processing nodes which constrain VMs from being placed in their ideal location and so on. When it is not possible to keep all data access local, one would like to optimize the placement of VMs so that the impact of data access latencies on completion times is minimized. We address this problem of optimized VM placement - given the location of the data sets, we need to determine the locations for placing the VMs so as to minimize data access latencies while satisfying system constraints. We present optimal algorithms for determining the VM locations satisfying various constraints and with objectives that capture natural tradeoffs between minimizing latencies and incurring bandwidth costs. We also consider the problem of incorporating inter-VM latency constraints. In this case, the associated location problem is NP-hard with no effective approximation within a factor of 2 - ϵ for any ϵ > 0. We discuss an effective heuristic for this case and evaluate by simulation the impact of the various tradeoffs in the optimization objectives.
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paper_title: A Mathematical Programming Approach for Server Consolidation Problems in Virtualized Data Centers

paper_content:

Today's data centers offer IT services mostly hosted on dedicated physical servers. Server virtualization provides a technical means for server consolidation. Thus, multiple virtual servers can be hosted on a single server. Server consolidation describes the process of combining the workloads of several different servers on a set of target servers. We focus on server consolidation with dozens or hundreds of servers, which can be regularly found in enterprise data centers. Cost saving is among the key drivers for such projects. This paper presents decision models to optimally allocate source servers to physical target servers while considering real-world constraints. Our central model is proven to be an NP-hard problem. Therefore, besides an exact solution method, a heuristic is presented to address large-scale server consolidation projects. In addition, a preprocessing method for server load data is introduced allowing for the consideration of quality-of-service levels. Extensive experiments were conducted based on a large set of server load data from a data center provider focusing on managerial concerns over what types of problems can be solved. Results show that, on average, server savings of 31 percent can be achieved only by taking cycles in the server workload into account.
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paper_title: A virtual machine consolidation framework for MapReduce enabled computing clouds

paper_content:

In nowadays computing clouds, it is of the cloud providers' economic interests to correctly consolidate the workload of the virtual machines (VMs) into the suitable physical servers in the cloud data center in order to minimize the total maintenance cost. However, during the consolidation process, sufficient protection should be provided to the service level agreement (SLA) of the VMs. In this paper, the VM consolidation problem for MapReduce enabled computing clouds has been investigated. In the MapReduce enabled computing clouds, MapReduce jobs are carried out by homogeneous MapReduce VM instances that have identical hardware resource. Two resource allocation schemes with corresponding SLA constraints for the MapReduce VMs and the non-MapReduce VMs are proposed. Based on these schemes, the VM consolidation problem is modeled as an integer nonlinear optimization problem and an efficient algorithm has been proposed to locate its solutions. The results show that better VM consolidation performance can be achieved by colocating MapReduce instances together with non-MapReduce instances in the same set of physical servers.
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paper_title: Reduce Energy Consumption through Virtual Machine Placement in Cloud Data Centre

paper_content:

In this paper, energy consumption in the data centre was studied where thousands of servers and other devices runs, energy are utilized to run the server and cooling the environment. Energy consumption can be reduced by switching off the idle server by means of migration of Virtual Machine from under-load Host. Load in cloud computing is maintained by migration of VM from the overloaded Host to a free Host or activate new Host. Based on this study, a reservation technique by using BIN packing was proposed in this paper with an overload detection algorithm. The proposed algorithm RBIN is experimented in 800 servers with 1024 Virtual Machines. From the experimental result, proposed method RBIN reduces energy in higher level.
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paper_title: CCAP: A Cache Contention-Aware Virtual Machine Placement Approach for HPC Cloud

paper_content:

Applications in High Performance Computing (HPC) cloud are characterized by large cache resource consumption due to large-scale inputs and intensive communications, which creates serious Shared Last Level cache (SLLC) performance bottleneck. Current system software stacks are not efficient in addressing this issue among virtual machines at the hypervisor level or the threads at the operating system level. In this paper, we investigate performance interference due to contention for SLLC in the HPC cloud. We employ an enhanced reuse distance analysis technique with an accelerated cyclic compression algorithm to identify application's cache interference intensity. Based on reuse distance analysis, we propose a practical Cache Contention-Aware virtual machine Placement approach (CCAP). CCAP dispatches virtual machines according to their cache interference intensities to avoid cache pollution and interference, thus alleviating negative effects of cache contention. We implement CCAP in the Xen hypervisor. Evaluation of NPB workload reveals that CCAP can improve performance of cache sensitive applications when they are co-scheduled with cache pollution programs. For a 2-workload system, it reduces execution time by 12 %, as well as cache miss rate by 13 %, while increasing throughput by 13 %, on average. Moreover, CCAP also improves the average performance of the cache pollution programs by 5 %. For a 4-workload system, CCAP brings more significant performance improvement to cache sensitive applications, an average increase of 20 %.
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paper_title: Workload characteristics-aware virtual machine consolidation algorithms

paper_content:

An energy conservation strategy must address two issues — placement of virtual machine images and workload characteristics of virtual machines. For performance reason most cloud systems copy a prototype image into the local disk of a physical machine before starting a virtual machine. If the physical machine that stores the image of a virtual machine is off-line, then we cannot run this virtual machine. The workload characteristics of virtual machines determine whether it is data-intensive or CPU-intensive. We assume that the system has a distributed file system therefore a physical machine can run any virtual machine even if it does not have the image. However, we observe that the performance of a data-intensive virtual machine running on a physical machine without its image could result in 60% performance loss compared with running the same virtual machine on a physical machine that has the virtual machine image. On the other hand, the performance of a CPU-intensive virtual machine is almost independent of whether the physical machine has the image or not. As a result, an energy conservation algorithm must consider the workload characteristic of a virtual machine when finding a physical machine to run it, especially for data-intensive virtual machines. This paper proposes a workload characteristics-aware virtual machine consolidation algorithms. We propose an approximation algorithm and two dynamic programmings to consolidate virtual machines and reduce the number of physical machines. We conduct experiments and compare the numbers of physical machines used by our approximation algorithm with the optimal number of physical machines found by our dynamic programming. The experiment results indicate that our approximation algorithm finds good solutions much faster than the dynamic programming.
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paper_title: An energy aware framework for virtual machine placement in cloud federated data centres

paper_content:

Data centres are powerful ICT facilities which constantly evolve in size, complexity, and power consumption. At the same time users' and operators' requirements become more and more complex. However, existing data centre frameworks do not typically take energy consumption into account as a key parameter of the data centre's configuration. To lower the power consumption while fulfilling performance requirements we propose a flexible and energy-aware framework for the (re)allocation of virtual machines in a data centre. The framework, being independent from the data centre management system, computes and enacts the best possible placement of virtual machines based on constraints expressed through service level agreements. The framework's flexibility is achieved by decoupling the expressed constraints from the algorithms using the Constraint Programming (CP) paradigm and programming language, basing ourselves on a cluster management library called Entropy. Finally, the experimental and simulation results demonstrate the effectiveness of this approach in achieving the pursued energy optimization goals.
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paper_title: A Relationship-Based VM Placement Framework of Cloud Environment

paper_content:

Managing computation resources in a cost-effective way has become the core competence for a Cloud provider to win over the market because of the "pay-as-you-go" business model. Therefore, VM placement has become more and more important in the research and practices of VM management by determining at what condition and on which physical server a VM should be placed so that the SLA can be guaranteed and servers' utilization can be improved. Much existing work simply formulates the above issue to be a bin-packing problem, which does not take the VM relationships into account. However, the relationship information can greatly impact the SLA of the Cloud system and the resource utilization. Therefore, in this paper, we propose a relationship-based VM placement framework, SmartCRS, to optimize the VM placement procedure. SmartCRS reveals the relationships between VMs automatically. Then by using such information and based on a constraint library, it gives a proper VM placement plan. Finally, the plan is carried out by SmartCRS automatically or by Cloud administrators manually to improve the server utilization and guarantee the required SLA. Two case studies are conducted to demonstrate the effectiveness and efficiency of the proposed framework at the end of this paper.
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paper_title: EQVMP: Energy-efficient and QoS-aware virtual machine placement for software defined datacenter networks

paper_content:

To provide effective and reliable services, cloud datacenters need parallel computing and virtualization techniques. This work presents an improved virtual machine (VM) placement mechanism, called Energy efficiency and Quality of Service (QoS) aware VM Placement (EQVMP) to overcome the problem of unbalanced traffic load in switching on and off VMs for the purpose of energy saving. EQVMP combines three key techniques: (1) hop reduction, (2) energy saving and (3) load balancing. Hop reduction can regroup VMs to lower the traffic load among them. Energy saving techniques aim at choosing the appropriate servers. The proposed load balancing updates VM placement periodically. Our experimental results show that the proposed scheme can lower energy consumption and maintain QoS. We propose an evaluation score [1] to assess VM placement in terms of energy, delay and throughput. Comparing to other existing placement policies, our proposed mechanism can enhance system throughput by 25% and can have better evaluation score.
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paper_title: Optimal update frequency model for physical machine state change and virtual machine placement in the cloud

paper_content:

Cloud computing is evolving into the default operational framework running modern data centers. Efficient data center operation is concerned with the total amount of energy consumed as well as assuring adequate resources are available to process all of the incoming work requests. Existing research has demonstrated several algorithms that can be used to determine the optimal number of resources required to service these requests. However, a key issue not addressed in these algorithms is determining the frequency of recalculating the number of required resources. Changing the required resources at a rate slower than the optimal update frequency results in lower energy efficiency due to the over allocation of resources. Changing the resources at a rate higher than the optimal frequency results in insufficient time for systems to change state, which results in SLA violations. In this paper, a stochastic optimization model is presented that determines the optimal update frequency for changing the states of the nodes of the cloud as well as determining the proper frequency for recalculating the maximum expected load, which improves the determination of the optimum number of resources required, therefore maximizes energy efficiency and minimizes SLA violations.
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paper_title: Network-aware virtual machine consolidation for large data centers

paper_content:

Resource management in modern data centers has become a challenging task due to the tremendous growth of data centers. In large virtual data centers, performance of applications is highly dependent on the communication bandwidth available among virtual machines. Traditional algorithms either do not consider network I/O details of the applications or are computationally intensive. We address the problem of identifying the virtual machine clusters based on the network traffic and placing them intelligently in order to improve the application performance and optimize the network usage in large data center. We propose a greedy consolidation algorithm that ensures the number of migrations is small and the placement decisions are fast, which makes it practical for large data centers. We evaluated our approach on real world traces from private and academic data centers, using simulation and compared the existing algorithms on various parameters like scheduling time, performance improvement and number of migrations. We observed a ~70% savings of the interconnect bandwidth and overall ~60% improvements in the applications performances. Also, these improvements were produced within a fraction of scheduling time and number of migrations.
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paper_title: Energy Efficient VM Placement Supported by Data Analytic Service

paper_content:

The popularity and commercial use of cloud computing has prompted an increased concern among cloud service providers for energy efficiency while still maintaining quality of service. One of the key techniques used for the efficient use of cloud server resources is virtual machine placement. This work introduces a precise VM placement algorithm that ensures energy efficiency and also prevents Service Level Agreement (SLA) violation. The mathematical model of the algorithm is supported by a sophisticated data analytic system implemented as a service. The precision of the algorithm is achieved by allowing each individual VM to build its own data model on demand over an appropriate time horizon. Thus the data model can reflect accurately the characteristics of resource usage of the VM. The algorithm can communicate synchronously or asynchronously with the data analytic service which is deployed as a cloud-based solution. In the experiments, several advanced data modelling and use forecasting techniques were evaluated. Results from simulation-based experiments show that the VM placement algorithm (supported by the data analytic service) can effectively reduce power consumption, the number of VM migrations, and prevent SLA violation, it also compares very favourably with other placement algorithms.
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paper_title: Evolutionary Optimal Virtual Machine Placement and Demand Forecaster for Cloud Computing

paper_content:

Cloud computing allows the users to efficiently and dynamically provision computing resources to meet their IT needs. Most cloud providers offer two types of payment plans to the user, i.e., reservation and on-demand. The reservation plan is typically cheaper than the on-demand plan but reservation plan has to be provisioned in advance. Reserving the resources would be straightforward if the actual computing demand (e.g., job processing) is known in advance. However, in reality, the actual computing demand can be observed only at the point of actual usage. Therefore, it is difficult to reserve the correct amount of resources during the reservation to meet the computing demands of the users. In this paper, we propose an evolutionary optimal virtual machine placement (EOVMP) algorithm with a demand forecaster. First, a demand forecaster predicts the computing demand. Then, EOVMP uses this predicted demand to allocate the virtual machines using reservation and on-demand plans for job processing. The performance of the proposed schemes is evaluated by simulations and numerical studies. The evaluation result shows that the EOVMP algorithm can provide the solution close to the optimal solution of stochastic integer programming (SIP) and the prediction of the demand forecaster is of reasonable accuracy.
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paper_title: Maximum migration time guarantees in dynamic server consolidation for virtualized data centers

paper_content:

Server consolidation is a vital mechanism in modern data centers in order to minimize expenses with infrastructure. In most cases, server consolidation may require migrating virtual machines between different physical servers. Although the downtime of live-migration is negligible, the amount of time to migrate all virtual machines can be substantial, delaying the completion of the consolidation process. This paper proposes a new server consolidation algorithm, which guarantees that migrations are completed in a given maximum time. The migration time is estimated using the max-min fairness model, in order to consider the competition of migration flows for the network infrastructure. The algorithm was simulated using a real workload and shows a good consolidation ratio in comparison to other algorithms, while also guaranteeing a maximum migration time.
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paper_title: A Stable Network-Aware VM Placement for Cloud Systems

paper_content:

Virtual Machine (VM) placement has to carefully consider the aggregated resource consumption of co-located VMs in order to obey service level agreements at lower possible cost. In this paper, we focus on satisfying the traffic demands of the VMs in addition to CPU and memory requirements. This is a much more complex problem both due to its quadratic nature (being the communication between a pair of VMs) and since it involves many factors beyond the physical host, like the network topologies and the routing scheme. Moreover, traffic patterns may vary over time and predicting the resulting effect on the actual available bandwidth between hosts within the data center is extremely difficult. We address this problem by trying to allocate a placement that not only satisfies the predicted communication demand but is also resilient to demand time-variations. This gives rise to a new optimization problem that we call the Min Cut Ratio-aware VM Placement (MCRVMP). The general MCRVMP problem is NP-Hard, hence, we introduce several heuristics to solve it in reasonable time. We present extensive experimental results, associated with both placement computation and run-time performance under time-varying traffic demands, to show that our heuristics provide good results (compared to the optimal solution) for medium size data centers.
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paper_title: PBFVMC: A New Pseudo-Boolean Formulation to Virtual-Machine Consolidation

paper_content:

Over the course of the last decade, there have been several improvements in the performance of Boolean Satisfiability (SAT), Integer Linear Programming (ILP) and Pseudo-Boolean Optimization (PBO) solvers. These improvements have encouraged the applications of SAT, ILP and PBO techniques in modeling complex engineering problems. One such problem is the Virtual Machine Consolidation. The Virtual Machine Consolidation problem consists in placing a set of virtual machines in a set of hardware in a way to increase workload on hardware where they can operate more energy-efficient. This paper proposes an improved PBO formulation of the Virtual Machine Consolidation problem, PBFVMC. The improved formulation and enhancements are built on top of a previous work and a new set of constraints is created and rationalized to work more friendly with current PBO solvers. It is observed that this new formulation goes a step ahead and more problems can now be solved.
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paper_title: QoS-Aware VM Placement in Multi-domain Service Level Agreements Scenarios

paper_content:

Virtualization technologies of Infrastructure-as-a- Service enable the live migration of running Virtual Machines (VMs) to achieve load balancing, fault-tolerance and hardware consolidation in data centers. However, the downtime/service unavailability due to live migration may be substantial with relevance to the customers' expectations on responsiveness, as the latter are declared in established Service Level Agreements (SLAs). Moreover, it may cause significant (potentially exponential) SLA violation penalties to its associated higher- level domains (Platform-as-a-Service and Software-as-a-Service). Therefore, VM live migration should be managed carefully. In this paper, we present the OpenStack version of the Generic SLA Manager, alongside its strategies for VM selection and allocation during live migration of VMs. We simulate a use case where IaaS (OpenStack-SLAM) and PaaS (OpenShift) are combined, and assess performance and efficiency of the aforementioned VM placement strategies, when a multi-domain SLA pricing & penalty model is involved. We find that our proposal is efficient in managing trade-offs between the operational objectives of service providers (including financial considerations) and the customers' expected QoS requirements.
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paper_title: Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center

paper_content:

Powerful data centers are the essential supporting infrastructure for mobile, ubiquitous, and cognitive computing, which are the most popular computing paradigms to utilize all kinds of physical resources and provide various services. To ensure the high quality of services, the performance and cost of a data center is a critical factor. In this paper, we investigate the issue of increasing the resource utilization of data centers to improve their performance and lower the cost. It is an efficient way to increase resource utilization via resource sharing. Technically, server virtualization provides the opportunity to share resources in data centers. However, it also introduces other problems, the primary problem being virtual machine placement (VMP), which is to choose a proper physical machine (PM) to deploy virtual machines (VMs) in runtime. We study the virtual machine placement problem with the target of minimizing the total energy consumption by the running of PMs, which is also an indication of resource utilization and the cost of a data center. Due to the multiple dimensionality of physical resources, there always exists a waste of resources, which results from the imbalanced use of multi-dimensional resources. To characterize the multi-dimensional resource usage states of PMs, we present a multi-dimensional space partition model. Based on this model, we then propose a virtual machine placement algorithm EAGLE, which can balance the utilization of multi-dimensional resources, reduce the number of running PMs, and thus lower the energy consumption. We also evaluate our proposed balanced algorithm EAGLE via extensive simulations and experiments on real traces. Experimental results show, over the long run, that EAGLE can save as much as 15% more energy than the first fit algorithm.
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paper_title: A Network-aware Virtual Machine Placement and Migration Approach in Cloud Computing

paper_content:

Cloud computing represents a major step up in computing whereby shared computation resources are provided on demand. In such a scenario, applications and data thereof can be hosted by various networked virtual machines (VMs). As applications, especially data-intensive applications, often need to communicate with data frequently, the network I/O performance would affect the overall application performance significantly. Therefore, placement of virtual machines which host an application and migration of these virtual machines while the unexpected network latency or congestion occurs is critical to achieve and maintain the application performance. To address these issues, this paper proposes a virtual machine placement and migration approach to minimizing the data transfer time consumption. Our simulation studies suggest that the proposed approach is effective in optimizing the data transfer between the virtual machine and data, thus helping optimize the overall application performance.
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paper_title: Power-Efficient Virtual Machine Placement and Migration in Data Centers

paper_content:

In this paper, we propose a power-efficient solution for virtual machine placement and migration in a fat tree data center network. This solution reduces power consumption as well as job delay by aggregating virtual machines to a few hyper visors and migrating communicating parties to close locations. In this work, we consider OpenFlow as the implementation protocol. In an OpenFlow environment, a centralized controller oversees job loads, virtual machine requirements and hardware availability. Given observation of such global knowledge, the OpenFlow controller can schedule jobs and distribute virtual machines accordingly. As jobs change and flows shift, the OpenFlow controller dynamically adjusts virtual machine assignments by aggregating virtual machines to close locations in order to save energy. With this placement and migration proposal, more jobs can operate concurrently with close sources and destinations of flows, thus both job and flow delay can be reduced.
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paper_title: Hierarchical Virtual Machine Consolidation in a Cloud Computing System

paper_content:

Improving the energy efficiency of cloud computing systems has become an important issue because the electric energy bill for 24/7 operation of these systems can be quite large. The focus of this paper is on the virtual machine (VM) consolidation in a cloud computing system as a way of lowering daily energy consumption of the system. In contrast to the existing works that assume resource demands of VMs are known and given as scalar variables, this paper treats these demands as random variables with known means and standard deviations. These random variables may be correlated with one another, and there are several kinds of resources which can be performance bottlenecks. Therefore, both the correlation and multiple resource type should be considered. The VM consolidation problem is then formulated as a multi-capacity stochastic bin packing problem. This problem is NP-hard, so we propose a heuristic method to solve the problem efficiently. The simulation results show that, in spite of its simplicity and scalability, the proposed method produces high quality solutions.
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paper_title: Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments

paper_content:

Server consolidation using virtualization technology has become increasingly important for improving data center efficiency. It enables one physical server to host multiple independent virtual machines (VMs), and the transparent movement of workloads from one server to another. Fine-grained virtual machine resource allocation and reallocation are possible in order to meet the performance targets of applications running on virtual machines. On the other hand, these capabilities create demands on system management, especially for large-scale data centers. In this paper, a two-level control system is proposed to manage the mappings of workloads to VMs and VMs to physical resources. The focus is on the VM placement problem which is posed as a multi-objective optimization problem of simultaneously minimizing total resource wastage, power consumption and thermal dissipation costs. An improved genetic algorithm with fuzzy multi-objective evaluation is proposed for efficiently searching the large solution space and conveniently combining possibly conflicting objectives. The simulation-based evaluation using power-consumption and thermal-dissipation models based on profiling of a Blade Center, demonstrates the good performance, scalability and robustness of our proposed approach. Compared with four well-known bin-packing algorithms and two single-objective approaches, the solutions obtained from our approach seek good balance among the conflicting objectives while others cannot.
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paper_title: Profiling Applications for Virtual Machine Placement in Clouds

paper_content:

Application profiling is an important technique for efficient resource management. The decision making of scheduling and resource allocation typically takes great advantage of such a technique primarily for improving resource utilization. With the advent of cloud computing as a multitenant virtualized platform, diverse applications are increasingly deployed onto the cloud and they more than often share physical resources. The background load (other applications running on the same physical machine) is therefore an important factor for profiling an application in this cloud computing scenario. In this paper, we present a novel application profiling technique using the canonical correlation analysis (CCA) method, which identifies the relationship between application performance and resource usage. We further devise a performance prediction model based on application profiles generated using CCA. Clearly, our profiling technique with this prediction model has a lot of potentials particularly in virtual machine (VM) placement with performance awareness. Our experimental results demonstrate the capability of our profiling technique and the accuracy of our prediction model.
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paper_title: Dynamic Energy-Efficient Virtual Machine Placement Optimization for Virtualized Clouds

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A virtual machine placement strategy based on the trade-off between energy consumption and SLA is presented. Aiming at dynamical changes of workload requirements, a self-adaptive placement strategy RLWR based on robust local weight regression is presented, which could decide the overload time of hosts dynamically. After detecting overloaded hosts, one virtual machine migration selection algorithm MNM is proposed. The MNM’s objective is to get minimal migration number. The migrated virtual machines are deployed using bin-packing algorithm PBFDH. The experimental results show that our algorithm has obvious advantages than other algorithms.
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paper_title: Interference-aware virtual machine placement in cloud computing systems

paper_content:

Cloud computing provides scalable computing and storage resources. These scalable resources can be dynamically organized as many virtual machines (VMs) to run user applications based on a pay-per-use basis. This paper investigates the interference-aware VM placement (IAVMP) problem. In addition to fully exploiting the resources of PMs, the IAVMP problem considers the quality of service (QoS) requirements of user applications and the VM interference reduction. We first formulate the IAVMP problem by an Integer Linear Programming (ILP) model to solve it optimally. Due to the computation complexity of the ILP model, we also propose a polynomial-time heuristic algorithm to efficiently solve the IAVMP problem. Finally, simulation experiments are performed to demonstrate the effectiveness of the proposed heuristic algorithm by comparing with other VM placement algorithms.
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paper_title: Server consolidation with migration control for virtualized data centers

paper_content:

Virtualization has become a key technology for simplifying service management and reducing energy costs in data centers. One of the challenges faced by data centers is to decide when, how, and which virtual machines (VMs) have to be consolidated into a single physical server. Server consolidation involves VM migration, which has a direct impact on service response time. Most of the existing solutions for server consolidation rely on eager migrations, which try to minimize the number of physical servers running VMs. These solutions generate unnecessary migrations due to unpredictable workloads that require VM resizing. This paper proposes an LP formulation and heuristics to control VM migration, which prioritize virtual machines with steady capacity. We performed experiments using TU-Berlin and Google data center workloads to compare our migration control strategy against existing eager-migration-based solutions. We observed that avoiding migration of VMs with steady capacity reduces the number of migrations with minimal penalty in the number of physical servers.
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paper_title: Inter-and-intra data center VM-placement for energy-efficient large-Scale cloud systems

paper_content:

Data centers play the crucial role in the delivery of Cloud services by enabling on-demand access to the shared resources such as software, platform, and infrastructure. Virtual Machine (VM) allocation is one of the challenging tasks in the data center management since user requirements, typically expressed as SLAs, have to be met with the minimum operational expenditure. Despite their huge processing and storage facilities, data centers are among the major contributors of the GreenHouse Gas (GHG) emissions of the IT services. In this paper, we propose a holistic approach for a large-scale Cloud system where the Cloud services are provisioned by several data centers interconnected over the backbone network. We propose a Mixed Integer Linear Programming (MILP) formulation that aims at virtualizing the backbone topology and placing the VMs in data centers with the objective of minimum power consumption. We compare our proposed solution with a benchmark MILP model which selects the closest data centers that can accommodate the user requests taking into account the CPU, memory, and bandwidth capacities of the residing physical hosts. Collected experimental results show the benefit of the proposed management scheme in terms of power consumption, resource utilization, and fairness for medium size data centers.
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paper_title: Provisioning of requests for virtual machine sets with placement constraints in IaaS clouds

paper_content:

We study the problem of optimising the provisioning of collections of virtual machines (VMs) having different placement constraints (e.g., security and anti-collocation) and characteristics (e.g., memory and disk capacity), given a set of physical machines (PMs) with known specifications, in order to achieve the objective of maximising an IaaS cloud provider's revenue. We propose two approaches. The first is based on the formulation of the problem as an integer linear programming (ILP) problem, the solution to which provides an optimal VM placement. The second approach is a heuristic based on classifying the requests into different categories and satisfying the constraints in a particular order using a first lit decreasing (FFD) algorithm for multi-dimensional vector bin packing problem. Given a model of VM placement constraints, offered resources and requests with multiple VM types, both approaches devise a placement plan in a way that maximizes revenue, having due regard both to customer requirements and PM capacities. We evaluate the relative performance of the solutions by means of numerical experiments. The results suggest the optimal solution is not practical for medium to large problems, but it is encouraging that the placement plans of the heuristic are close to those of the optimal solution for smaller problem sizes. We use the heuristic to generate results for large scale placement problems; experiments suggest that it is practical in terms of its runtime efficiency and can provide an effective means of online VM-to-PM mapping.
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paper_title: Efficient VM placement with multiple deterministic and stochastic resources in data centers

paper_content:

Virtual machines (VMs) may significantly improve the efficiency of data center infrastructure by sharing resources of physical servers. This benefit relies on an efficient VM placement scheme to minimize the number of required servers. Existing VM placement algorithms usually assume that VMs' demands for resources are deterministic and stable. However, for certain resources, such as network bandwidth, VMs' demands are bursty and time varying, and demonstrate stochastic nature. In this paper, we study efficient VM placement in data centers with multiple deterministic and stochastic resources. First, we formulate the Multidimensional Stochastic VM Placement (MSVP) problem, with the objective to minimize the number of required servers and at the same time satisfy a predefined resource availability guarantee. Then, we show that the problem is NP-hard, and propose a polynomial time algorithm called Max-Min Multidimensional Stochastic Bin Packing (M3SBP). The basic idea is to maximize the minimum utilization ratio of all the resources of a server, while satisfying the demands of VMs for both deterministic and stochastic resources. Next, we conduct simulations to evaluate the performance of M3SBP. The results demonstrate that M3SBP guarantees the availability requirement for stochastic resources, and M3SBP needs the smallest number of servers to provide the guarantee among the benchmark algorithms.
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paper_title: Data-Aware Virtual Machine Placement and Rate Allocation in Cloud Environment

paper_content:

Infrastructure-as-a-service cloud provides a suitable environment where cloud users can run data-intensive applications and store their required data files. The performance of data-intensive applications strongly depends on data transmission delay. This delay is a function of the size of data files, the location of virtual machines that run the applications as well as the allocation of data rates to virtual machines. In this paper, we propose a novel virtual machine placement algorithm that jointly optimizes the placement of virtual machines as well as their allocated data rates. Through Simulation results we show that the proposed algorithm can significantly reduce the data transfer delay for virtual machines compared to placement algorithms previously proposed in the literature.
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paper_title: Energy efficient VM placement heuristic algorithms comparison for cloud with multidimensional resources

paper_content:

Cloud computing provides user utility-oriented IT services, yet accompanied with huge energy consuming, which contributes to the high operational cost as well as CO2 emission. Making Cloud computing energy efficient can lead to a better tradeoff between profit and environmental impact. In this paper, we formulate the energy efficient VM placement problem in Cloud architecture with multidimensional resources and introduce the objective of this problem. Heuristic algorithms including three traditional local search algorithms and generic algorithm (GA) are presented to provide possible optimized solution. We conduct experiments based on Cloudsim. The result shows that GA sometimes provide the best solution, but with poor stabability. Although the BF provide neither the best nor the worst solution most of time, it have the best stabability.
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paper_title: Energy-Efficient Virtual Machine Replication and Placement in a Cloud Computing System

paper_content:

By utilizing Virtual Machines (VM) and doing server consolidation in a datacenter, a cloud provider can reduce the total energy consumption for servicing his clients with little performance degradation. In particular, the cloud provider can take advantage of dissimilar workloads and by assigning these workloads to the same server, can utilize fewer active servers to service his clients. Placing multiple copies of a VM on different servers and distributing the incoming requests among these VM copies can reduce the resource requirement for each VM copy and help the cloud provider utilize the servers more efficiently. In this paper, the problem of energy-efficient VM placement in a cloud computing system is solved. Precisely, we present an approach that first creates multiple copies of VMs and then uses dynamic programming and local search to place these copies on the physical servers. Simulation results show that the proposed algorithm reduces the total energy consumption by up to 20% with respect to previous work.
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paper_title: Optimizing VM placement for HPC in the cloud

paper_content:

Computing as a service model in cloud has encouraged High Performance Computing to reach out to wider scientific and industrial community. Many small and medium scale HPC users are exploring Infrastructure cloud as a possible platform to run their applications. However, there are gaps between the characteristic traits of an HPC application and existing cloud scheduling algorithms. In this paper, we propose an HPC-aware scheduler and implement it atop Open Stack scheduler. In particular, we introduce topology awareness and consideration for homogeneity while allocating VMs. We demonstrate the benefits of these techniques by evaluating them on a cloud setup on Open Cirrus test-bed.
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paper_title: Dynamic optimization of virtual machine placement by resource usage prediction

paper_content:

In order to save energy with preventing the lack of the resources on servers in data centers, we address the dynamic optimization of virtual machine placement. A decision problem of the virtual machine placement has been formulated as Bin Packing problem. Conventional methods repeat to solve the problem at regular intervals. Live migration is used for changing the virtual machine placement, but some of live migrations are unnecessary; the virtual machine is migrated repeatedly between the physical servers. For the purpose of reducing the unnecessary live migrations, we propose a dynamic optimization of virtual machine placement by resource usage prediction. The proposed method predicts the future resource usage by Auto Regressive Model. The proposed method decides the virtual machine placement by solving the Bin Packing problem with the predicted resource usage.
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paper_title: An Optimization-Based Scheme for Efficient Virtual Machine Placement

paper_content:

According to the important methodology of convex optimization theory, the energy-efficient and scalability problems of modern data centers are studied. Then a novel virtual machine (VM) placement scheme is proposed for solving these problems in large scale. Firstly, by referring the definition of VM placement fairness and utility function, the basic algorithm of VM placement which fulfills server constraints of physical machines is discussed. Then, we abstract the VM placement as an optimization problem which considers the inherent dependencies and traffic between VMs. By given the structural differences of recently proposed data center architectures, we further investigate a comparative analysis on the impact of the network architectures, server constraints and application dependencies on the potential performance gain of optimization-based VM placement. Comparing with the existing schemes, the performance improvements are illustrated from multiple perspectives, such as reducing the number of physical machines deployment, decreasing communication cost between VMs, improving energy-efficient and scalability of data centers.
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paper_title: Server Consolidation Algorithms with Bounded Migration Cost and Performance Guarantees in Cloud Computing

paper_content:

Consolidation of virtual machines is essential to achieve energy optimization in cloud computing environments. As virtual machines dynamically enter and leave a cloud system, it becomes necessary to relocate virtual machines among servers. However, relocation of virtual machines introduces run-time overheads and consumes extra energy, thus an careful planning for relocation is necessary. We model the relocation problem as a modified bin packing problem and propose a new server consolidation algorithm that guarantees server consolidation with {\em bounded} relocation costs. We also conduct a detailed analysis on the complexity of the server consolidation problem, and give a upper bound on the cost of relocation. Finally, we conduct simulations and compare our server consolidation algorithm with other relocation methods, like First Fit and Best Fit method. The experiment results suggest an interesting trade-off between server consolidation quality and relocation cost. Our algorithm is able to trade about 1\% in server consolidation quality for a reduction about 50\% in relocation cost, when compared with other well known bin packing algorithms. We also note that the relocation cost incurred in our method is much less than the theoretical bound we provided. The reason is that we overestimate the amount of relocation from theoretical analysis, and the actual amount of relocation found from experiments is much less than the worst-case bound from theoretical analysis.
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paper_title: Multi-objective Virtual Machine Placement with Service Level Agreement: A Memetic Algorithm Approach

paper_content:

The process of selecting which virtual machines should be located (i.e. executed) at each physical machine of a Datacenter is known as Virtual Machine Placement VMP. This work proposes for the first time a multi-objective formulation of the VMP considering Service Level Agreement. A novel multiobjective memetic algorithm is also proposed to solve the formulated multi-objective problem. This proposal is validated comparing experimental results of the proposed algorithm with a brute force exhaustive search algorithm. Simulations prove the correctness of the proposed memetic algorithm and its scalability considering different experimental scenarios.
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paper_title: Optimal virtual machine placement across multiple cloud providers

paper_content:

Cloud computing provides users an efficient way to dynamically allocate computing resources to meet demands. Cloud providers can offer users two payment plans, i.e., reservation and on-demand plans for resource provisioning. Price of resources in reservation plan is generally cheaper than that in on-demand plan. However, since the reservation plan has to be acquired in advance, it may not fully meet future demands in which the on-demand plan can be used to guarantee the availability to the user. In this paper, we propose an optimal virtual machine placement (OVMP) algorithm. This algorithm can minimize the cost spending in each plan for hosting virtual machines in a multiple cloud provider environment under future demand and price uncertainty. OVMP algorithm makes a decision based on the optimal solution of stochastic integer programming (SIP) to rent resources from cloud providers. The performance of OVMP algorithm is evaluated by numerical studies and simulation. The results clearly show that the proposed OVMP algorithm can minimize users' budgets. This algorithm can be applied to provision resources in emerging cloud computing environments.
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paper_title: An Energy Efficient Virtual Machine Placement Algorithm with Balanced Resource Utilization

paper_content:

Virtualization is a key technology for resource sharing in IaaS/PaaS cloud infrastructures. One primary issue in virtualization is the virtual machine placement (VMP) problem, which is to choose proper physical machine (PM) to deploy virtual machines (VMs) in runtime. In this paper, we study the VMP problem with the goal of minimizing the total energy consumption. We first present a multi-dimensional space partition model to characterize the resource usage states of PMs. Based on this model, we then propose a virtual machine placement algorithm, which can balance the utilization of multi-dimensional resources, reduce the number of running PMs and thus lower down the energy consumption. We also evaluate our proposed balanced algorithm via extensive simulations. Simulation results show that our approach can save as much as 15% energy compared to the first fit algorithm over a long run.
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paper_title: Towards Profitable Virtual Machine Placement in the Data Center

paper_content:

Motivated by the limit on the power usage effectiveness (PUE) of the data centers, the potential benefit of the consolidation, and the impetus of achieving maximum return on investment (ROI) on the cloud computing market, we investigate VM placement in the data center, formulate a multi-level generalized assignment problem (MGAP) for maximizing the profit under the service level agreement and the power budget constraint based on the model of a virtualized data center, and solve it with a first-fit heuristic. Numerical simulations show that the first-fit heuristic is effective in solving the large-scale instances of the MGAP with the sampled simulation setups.
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paper_title: Multiobjective Virtual Machine Placement in Cloud Environment

paper_content:

In this paper, the virtual machine placement problem is formulated as a multi-objective optimization problem. The objectives are maximizing profit, maximizing load balancing and minimizing recourse wastage. Results of Genetic algorithms, Non-dominated Sorting Genetic Algorithm and Non-dominated Sorting Genetic Algorithm-II are compared with common solution representation, penalty and benefit values. All the three algorithms reported good solutions whereas GA and NSGA are subjected to premature convergence and duplicate solutions. NSGA-II gives good and diversified range of solutions.
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paper_title: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing

paper_content:

Virtual machine placement is a process of mapping virtual machines to physical machines. The optimal placement is important for improving power efficiency and resource utilization in a cloud computing environment. In this paper, we propose a multi-objective ant colony system algorithm for the virtual machine placement problem. The goal is to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption. The proposed algorithm is tested with some instances from the literature. Its solution performance is compared to that of an existing multi-objective genetic algorithm and two single-objective algorithms, a well-known bin-packing algorithm and a max-min ant system (MMAS) algorithm. The results show that the proposed algorithm is more efficient and effective than the methods we compared it to.
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paper_title: Design and Implementation of a Multi-objective Optimization Mechanism for Virtual Machine Placement in Cloud Computing Data Center

paper_content:

Cloud computing is becoming a popular way of supplying and using computing resources. A cloud-computing data center is equipped with a large number of physical resources and must manage an even larger number of virtual machines (VMs). The center’s VM placement strategy affects the utilization of physical resources, and consequently, it influences operational costs. Our goal is to develop a multi-objective optimization mechanism for VM placement that satisfies various constraints and results in the lowest operational cost. The number of possible combinations of VMs and hosts can be extremely large. For the mechanism to be practical, the number of possible combinations must be reduced. We reduced computational overheads by classifying VM hosts into a relatively small number of equivalent sets. Simulation results show that expected operational costs can be significantly reduced by applying the proposed mechanism.
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paper_title: An Availability-Aware Virtual Machine Placement Approach for Dynamic Scaling of Cloud Applications

paper_content:

Cloud computing promises customers the on-demand ability to dynamically provision virtualization resources in face of workload variations. Most existing scaling approaches addressed this problem by allocating application to a certain amount of cloud resources. However, the problem of the availability of application influenced by VM-based physical locations during resource scaling process is a serious challenge due to dynamic complex workload and has not been widely discussed yet. In this paper, we present a novel availability-based computing model to describe availability attribute of one application in the hierarchical structured cloud. Moreover, we propose an availability-aware policy by performing both vertical and horizontal scaling to explore how and where to allocate computing resource. Simulation results indicate that our model captured the availability of cloud applications properly and proposed scaling approach achieves the objectives of meeting availability demands and minimizing the total communication cost.
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paper_title: CCAP: A Cache Contention-Aware Virtual Machine Placement Approach for HPC Cloud

paper_content:

Applications in High Performance Computing (HPC) cloud are characterized by large cache resource consumption due to large-scale inputs and intensive communications, which creates serious Shared Last Level cache (SLLC) performance bottleneck. Current system software stacks are not efficient in addressing this issue among virtual machines at the hypervisor level or the threads at the operating system level. In this paper, we investigate performance interference due to contention for SLLC in the HPC cloud. We employ an enhanced reuse distance analysis technique with an accelerated cyclic compression algorithm to identify application's cache interference intensity. Based on reuse distance analysis, we propose a practical Cache Contention-Aware virtual machine Placement approach (CCAP). CCAP dispatches virtual machines according to their cache interference intensities to avoid cache pollution and interference, thus alleviating negative effects of cache contention. We implement CCAP in the Xen hypervisor. Evaluation of NPB workload reveals that CCAP can improve performance of cache sensitive applications when they are co-scheduled with cache pollution programs. For a 2-workload system, it reduces execution time by 12 %, as well as cache miss rate by 13 %, while increasing throughput by 13 %, on average. Moreover, CCAP also improves the average performance of the cache pollution programs by 5 %. For a 4-workload system, CCAP brings more significant performance improvement to cache sensitive applications, an average increase of 20 %.
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paper_title: A Stable Network-Aware VM Placement for Cloud Systems

paper_content:

Virtual Machine (VM) placement has to carefully consider the aggregated resource consumption of co-located VMs in order to obey service level agreements at lower possible cost. In this paper, we focus on satisfying the traffic demands of the VMs in addition to CPU and memory requirements. This is a much more complex problem both due to its quadratic nature (being the communication between a pair of VMs) and since it involves many factors beyond the physical host, like the network topologies and the routing scheme. Moreover, traffic patterns may vary over time and predicting the resulting effect on the actual available bandwidth between hosts within the data center is extremely difficult. We address this problem by trying to allocate a placement that not only satisfies the predicted communication demand but is also resilient to demand time-variations. This gives rise to a new optimization problem that we call the Min Cut Ratio-aware VM Placement (MCRVMP). The general MCRVMP problem is NP-Hard, hence, we introduce several heuristics to solve it in reasonable time. We present extensive experimental results, associated with both placement computation and run-time performance under time-varying traffic demands, to show that our heuristics provide good results (compared to the optimal solution) for medium size data centers.
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paper_title: QoS-Aware VM Placement in Multi-domain Service Level Agreements Scenarios

paper_content:

Virtualization technologies of Infrastructure-as-a- Service enable the live migration of running Virtual Machines (VMs) to achieve load balancing, fault-tolerance and hardware consolidation in data centers. However, the downtime/service unavailability due to live migration may be substantial with relevance to the customers' expectations on responsiveness, as the latter are declared in established Service Level Agreements (SLAs). Moreover, it may cause significant (potentially exponential) SLA violation penalties to its associated higher- level domains (Platform-as-a-Service and Software-as-a-Service). Therefore, VM live migration should be managed carefully. In this paper, we present the OpenStack version of the Generic SLA Manager, alongside its strategies for VM selection and allocation during live migration of VMs. We simulate a use case where IaaS (OpenStack-SLAM) and PaaS (OpenShift) are combined, and assess performance and efficiency of the aforementioned VM placement strategies, when a multi-domain SLA pricing & penalty model is involved. We find that our proposal is efficient in managing trade-offs between the operational objectives of service providers (including financial considerations) and the customers' expected QoS requirements.
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paper_title: Efficient VM placement with multiple deterministic and stochastic resources in data centers

paper_content:

Virtual machines (VMs) may significantly improve the efficiency of data center infrastructure by sharing resources of physical servers. This benefit relies on an efficient VM placement scheme to minimize the number of required servers. Existing VM placement algorithms usually assume that VMs' demands for resources are deterministic and stable. However, for certain resources, such as network bandwidth, VMs' demands are bursty and time varying, and demonstrate stochastic nature. In this paper, we study efficient VM placement in data centers with multiple deterministic and stochastic resources. First, we formulate the Multidimensional Stochastic VM Placement (MSVP) problem, with the objective to minimize the number of required servers and at the same time satisfy a predefined resource availability guarantee. Then, we show that the problem is NP-hard, and propose a polynomial time algorithm called Max-Min Multidimensional Stochastic Bin Packing (M3SBP). The basic idea is to maximize the minimum utilization ratio of all the resources of a server, while satisfying the demands of VMs for both deterministic and stochastic resources. Next, we conduct simulations to evaluate the performance of M3SBP. The results demonstrate that M3SBP guarantees the availability requirement for stochastic resources, and M3SBP needs the smallest number of servers to provide the guarantee among the benchmark algorithms.
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paper_title: Energy efficient VM placement heuristic algorithms comparison for cloud with multidimensional resources

paper_content:

Cloud computing provides user utility-oriented IT services, yet accompanied with huge energy consuming, which contributes to the high operational cost as well as CO2 emission. Making Cloud computing energy efficient can lead to a better tradeoff between profit and environmental impact. In this paper, we formulate the energy efficient VM placement problem in Cloud architecture with multidimensional resources and introduce the objective of this problem. Heuristic algorithms including three traditional local search algorithms and generic algorithm (GA) are presented to provide possible optimized solution. We conduct experiments based on Cloudsim. The result shows that GA sometimes provide the best solution, but with poor stabability. Although the BF provide neither the best nor the worst solution most of time, it have the best stabability.
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paper_title: Optimizing VM placement for HPC in the cloud

paper_content:

Computing as a service model in cloud has encouraged High Performance Computing to reach out to wider scientific and industrial community. Many small and medium scale HPC users are exploring Infrastructure cloud as a possible platform to run their applications. However, there are gaps between the characteristic traits of an HPC application and existing cloud scheduling algorithms. In this paper, we propose an HPC-aware scheduler and implement it atop Open Stack scheduler. In particular, we introduce topology awareness and consideration for homogeneity while allocating VMs. We demonstrate the benefits of these techniques by evaluating them on a cloud setup on Open Cirrus test-bed.
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paper_title: Multiobjective Virtual Machine Placement in Cloud Environment

paper_content:

In this paper, the virtual machine placement problem is formulated as a multi-objective optimization problem. The objectives are maximizing profit, maximizing load balancing and minimizing recourse wastage. Results of Genetic algorithms, Non-dominated Sorting Genetic Algorithm and Non-dominated Sorting Genetic Algorithm-II are compared with common solution representation, penalty and benefit values. All the three algorithms reported good solutions whereas GA and NSGA are subjected to premature convergence and duplicate solutions. NSGA-II gives good and diversified range of solutions.
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Title: Virtual Machine Placement Literature Review
Section 1: BACKGROUND AND MOTIVATION
Description 1: Provide an introduction to the concept of Virtual Machine Placement (VMP) and discuss its importance within datacenters, referencing relevant surveys and identifying key open challenges in the domain.

Section 2: REVIEWED LITERATURE
Description 2: Outline the process undertaken to select relevant research articles for review, including the search criteria and the filtering steps used to narrow down the literature.

Section 3: LITERATURE OVERVIEW
Description 3: Summarize the current state of VMP literature, noting any general trends, major findings, and specifics on how VMP problems have historically been addressed in various studies.

Section 4: OPTIMIZATION APPROACHES
Description 4: Present an overview of the different optimization approaches used in the reviewed articles to solve VMP problems, categorizing them into mono-objective, multi-objective solved as mono-objective, and pure multi-objective approaches.

Section 5: OBJECTIVE FUNCTIONS
Description 5: Discuss the different objective functions proposed in the literature for optimizing VMP, breaking them down into categories such as energy consumption minimization, network traffic minimization, economical costs optimization, performance maximization, and resource utilization maximization.

Section 6: SOLUTION TECHNIQUES
Description 6: Describe the various solution techniques employed in the VMP literature, including deterministic algorithms, heuristics, meta-heuristics, and approximation algorithms.

Section 7: FORMULATION: OFFLINE OR ONLINE
Description 7: Explain the distinction between offline and online VMP formulations and discuss the prevalence of each type in the reviewed articles.

Section 8: CLOUD ARCHITECTURES
Description 8: Examine the types of cloud architectures considered in the VMP studies, including single-cloud, multi-cloud, and federated clouds.

Section 9: ORIENTATION: PROVIDER-ORIENTED OR BROKER-ORIENTED
Description 9: Compare provider-oriented and broker-oriented perspectives in VMP problem formulations and discuss their implications for cloud infrastructure optimization.

Section 10: EXPERIMENTAL ENVIRONMENT
Description 10: Review the experimental environments used in the VMP literature, highlighting types of experiments, workload types and distributions, and the scale of the experiments.

Section 11: CONCLUSIONS
Description 11: Summarize the findings of the literature review, noting major contributions and identifying potential future research directions in the field of VMP.