A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing
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paper_title: Server virtualization architecture and implementation

paper_content:

Virtual machine technology, or virtualization, is gaining momentum in the information technology community. While virtual machines are not a new concept, recent advances in hardware and software technology have brought virtualization to the forefront of IT management. Stability, cost savings, and manageability are among the reasons for the recent rise of virtualization. Virtual machine solutions can be classified by hardware, software, and operating system/containers. From its inception on the mainframe to distributed servers on x86, the virtual machine has matured and will play an increasing role in systems management.
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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: Allocation of Virtual Machines in Cloud Data Centers—A Survey of Problem Models and Optimization Algorithms

paper_content:

Data centers in public, private, and hybrid cloud settings make it possible to provision virtual machines (VMs) with unprecedented flexibility. However, purchasing, operating, and maintaining the underlying physical resources incurs significant monetary costs and environmental impact. Therefore, cloud providers must optimize the use of physical resources by a careful allocation of VMs to hosts, continuously balancing between the conflicting requirements on performance and operational costs. In recent years, several algorithms have been proposed for this important optimization problem. Unfortunately, the proposed approaches are hardly comparable because of subtle differences in the used problem models. This article surveys the used problem formulations and optimization algorithms, highlighting their strengths and limitations, and pointing out areas that need further research.
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paper_title: A Review on Load Balancing of Virtual Machine Resources in Cloud Computing

paper_content:

An effective load balance (LB) management achieves high performance computing (HPC) and green computing. Users can run their jobs on virtual machines (VMs). Virtual machine (VM) has own resources (CPU and memory). VM migrates from host to another host during fail of VM, hot spot and high resource demand. Effective LB management is based on scheduling policy and management Strategies. In this paper it is discussed the available scheduling mechanisms, goals and strategies of load balancing techniques. The aim of this work to elaborate the key analysis of research works on LB.
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paper_title: Live Migration of Multiple Virtual Machines with Resource Reservation in Cloud Computing Environments

paper_content:

Virtualization technology is currently becoming increasingly popular and valuable in cloud computing environments due to the benefits of server consolidation, live migration, and resource isolation. Live migration of virtual machines can be used to implement energy saving and load balancing in cloud data center. However, to our knowledge, most of the previous work concentrated on the implementation of migration technology itself while didn't consider the impact of resource reservation strategy on migration efficiency. This paper focuses on the live migration strategy of multiple virtual machines with different resource reservation methods. We first describe the live migration framework of multiple virtual machines with resource reservation technology. Then we perform a series of experiments to investigate the impacts of different resource reservation methods on the performance of live migration in both source machine and target machine. Additionally, we analyze the efficiency of parallel migration strategy and workload-aware migration strategy. The metrics such as downtime, total migration time, and workload performance overheads are measured. Experiments reveal some new discovery of live migration of multiple virtual machines. Based on the observed results, we present corresponding optimization methods to improve the migration efficiency.
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paper_title: Server-storage virtualization: integration and load balancing in data centers

paper_content:

We describe the design of an agile data center with integrated server and storage virtualization technologies. Such data centers form a key building block for new cloud computing architectures. We also show how to leverage this integrated agility for non-disruptive load balancing in data centers across multiple resource layers - servers, switches, and storage. We propose a novel load balancing algorithm called VectorDot for handling the hierarchical and multi-dimensional resource constraints in such systems. The algorithm, inspired by the successful Toyoda method for multi-dimensional knapsacks, is the first of its kind. We evaluate our system on a range of synthetic and real data center testbeds comprising of VMware ESX servers, IBM SAN Volume Controller, Cisco and Brocade switches. Experiments under varied conditions demonstrate the end-to-end validity of our system and the ability of VectorDot to efficiently remove overloads on server, switch and storage nodes.
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paper_title: Cost of Virtual Machine Live Migration in Clouds: A Performance Evaluation

paper_content:

Virtualization has become commonplace in modern data centers, often referred as "computing clouds". The capability of virtual machine live migration brings benefits such as improved performance, manageability and fault tolerance, while allowing workload movement with a short service downtime. However, service levels of applications are likely to be negatively affected during a live migration. For this reason, a better understanding of its effects on system performance is desirable. In this paper, we evaluate the effects of live migration of virtual machines on the performance of applications running inside Xen VMs. Results show that, in most cases, migration overhead is acceptable but cannot be disregarded, especially in systems where availability and responsiveness are governed by strict Service Level Agreements. Despite that, there is a high potential for live migration applicability in data centers serving modern Internet applications. Our results are based on a workload covering the domain of multi-tier Web 2.0 applications.
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paper_title: Live migration of virtual machines

paper_content:

Migrating operating system instances across distinct physical hosts is a useful tool for administrators of data centers and clusters: It allows a clean separation between hard-ware and software, and facilitates fault management, load balancing, and low-level system maintenance. By carrying out the majority of migration while OSes continue to run, we achieve impressive performance with minimal service downtimes; we demonstrate the migration of entire OS instances on a commodity cluster, recording service downtimes as low as 60ms. We show that that our performance is sufficient to make live migration a practical tool even for servers running interactive loads. In this paper we consider the design options for migrating OSes running services with liveness constraints, focusing on data center and cluster environments. We introduce and analyze the concept of writable working set, and present the design, implementation and evaluation of high-performance OS migration built on top of the Xen VMM.
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paper_title: A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing

paper_content:

Virtual machine (VM) scheduling with load balancing in cloud computing aims to assign VMs to suitable servers and balance the resource usage among all of the servers. In an infrastructure-as-a-service framework, there will be dynamic input requests, where the system is in charge of creating VMs without considering what types of tasks run on them. Therefore, scheduling that focuses only on fixed task sets or that requires detailed task information is not suitable for this system. This paper combines ant colony optimization and particle swarm optimization to solve the VM scheduling problem, with the result being known as ant colony optimization with particle swarm (ACOPS). ACOPS uses historical information to predict the workload of new input requests to adapt to dynamic environments without additional task information. ACOPS also rejects requests that cannot be satisfied before scheduling to reduce the computing time of the scheduling procedure. Experimental results indicate that the proposed algorithm can keep the load balance in a dynamic environment and outperform other approaches.
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paper_title: Energy Efficient Multi Dimensional Host Load Aware Algorithm for Virtual Machine Placement and Optimization in Cloud Environment

paper_content:

The effectiveness and elasticity of virtual machine placement has become a main concern in modern cloud computing environment. Mapping the virtual machines to the physical machines cluster is called the VM placement. In this paper we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We used two different techniques, first initial VM packing is done by checking the load of the physical host and the user constraints of the VMs. Second optimization of placed VMs is done by using a hybrid genetic algorithm based on fitness function. The presented algorithm is implemented in JAVA Net beans IDE, and Clouds simulator has been used for simulation to assess the execution and performance of our heuristics by comparison with algorithms first fit, best fit and round robin. The performance of the proposed algorithm was examined from both users and service provider’s perception. The simulation results show that our proposed algorithm uses the less number of physical servers for placing a certain number of VMs which helps to improve the resource utilization rate. The response time of our algorithm is little bit more than the first fit algorithm because of its nature of allocating VMs is based on the user constraints and past usage history of the VMs. Elevated SLA satisfaction rate and inferior load imbalance rate was observed in results. Since we used a modified version of hybrid genetic algorithm for load optimization the percentage of VM migrations had been decreased through which we can achieve the better results for load balancing along with cost reduction. The results also show that our hybrid genetic based multi dimensional host load aware and user constraints based algorithm is applicable, valuable and reliable for implementation in real data center environments.
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paper_title: Multi-Cloud: expectations and current approaches

paper_content:

Using resources and services from multiple Clouds is a natural evolution from consuming the ones from in-silo Clouds. Technological and administrative barriers are however slowing the process. Fortunately the recent years are marked by the appearance of several solutions that are partially overpassing them. However, the approaches are quite various and not adopted at large scale. This paper intends to offer a snapshot of the current state-of-the-art and to identify the future steps in building Multi-Clouds. A list of basic requirements for a Multi-Cloud is proposed.
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paper_title: A dynamic and integrated load-balancing scheduling algorithm for Cloud datacenters

paper_content:

One of the challenging scheduling problems in Cloud datacenters is to take the allocation and migration of reconfigurable virtual machines into consideration as well as the integrated features of hosting physical machines. We introduce a dynamic and integrated resource scheduling algorithm (DAIRS) for Cloud datacenters. Unlike traditional load-balance scheduling algorithms which consider only one factor such as the CPU load in physical servers, DAIRS treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop integrated measurement for the total imbalance level of a Cloud datacenter as well as the average imbalance level of each server. Simulation results show that DAIRS has good performance with regard to total imbalance level, average imbalance level of each server, as well as overall running time.
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paper_title: A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment

paper_content:

The current virtual machine(VM) resources scheduling in cloud computing environment mainly considers the current state of the system but seldom considers system variation and historical data, which always leads to load imbalance of the system. In view of the load balancing problem in VM resources scheduling, this paper presents a scheduling strategy on load balancing of VM resources based on genetic algorithm. According to historical data and current state of the system and through genetic algorithm, this strategy computes ahead the influence it will have on the system after the deployment of the needed VM resources and then chooses the least-affective solution, through which it achieves the best load balancing and reduces or avoids dynamic migration. This strategy solves the problem of load imbalance and high migration cost by traditional algorithms after scheduling. Experimental results prove that this method is able to realize load balancing and reasonable resources utilization both when system load is stable and variant.
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paper_title: An online load balancing scheduling algorithm for cloud data centers considering real-time multi-dimensional resource

paper_content:

In general, load-balance scheduling is NP-hard problem as proved in many open literatures. We introduce an online load balancing resource scheduling algorithm (OLRSA) for Cloud datacenters considering real-time and multi-dimensional resources. Unlike traditional load balance scheduling algorithms which often consider only one factor such as the CPU load in physical servers, OLRSA treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop and apply integrated measurement for each server and a Cloud datacenter. Simulation results show that OLRSA has better performance than a few related load-balancing algorithms with regard to total imbalance level, makespan, as well as overall load efficiency.
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paper_title: A comparison of centralized and distributed meta-scheduling architectures for computation and communication tasks in Grid networks

paper_content:

The management of Grid resources requires scheduling of both computation and communication tasks at various levels. In this study, we consider the two constituent sub-problems of Grid scheduling, namely: (i) the scheduling of computation tasks to processing resources and (ii) the routing and scheduling of the data movement in a Grid network. Regarding computation tasks, we examine two typical online task scheduling algorithms that employ advance reservations and perform full network simulation experiments to measure their performance when implemented in a centralized or distributed manner. Similarly, for communication tasks, we compare two routing and data scheduling algorithms that are implemented in a centralized or a distributed manner. We examine the effect network propagation delay has on the performance of these algorithms. Our simulation results indicate that a distributed architecture with an exhaustive resource utilization update strategy yields better average end-to-end delay performance than a centralized architecture.
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paper_title: CloudCmp: comparing public cloud providers

paper_content:

While many public cloud providers offer pay-as-you-go computing, their varying approaches to infrastructure, virtualization, and software services lead to a problem of plenty. To help customers pick a cloud that fits their needs, we develop CloudCmp, a systematic comparator of the performance and cost of cloud providers. CloudCmp measures the elastic computing, persistent storage, and networking services offered by a cloud along metrics that directly reflect their impact on the performance of customer applications. CloudCmp strives to ensure fairness, representativeness, and compliance of these measurements while limiting measurement cost. Applying CloudCmp to four cloud providers that together account for most of the cloud customers today, we find that their offered services vary widely in performance and costs, underscoring the need for thoughtful provider selection. From case studies on three representative cloud applications, we show that CloudCmp can guide customers in selecting the best-performing provider for their applications.
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paper_title: Virtual machine mapping policy based on load balancing in private cloud environment

paper_content:

The virtual machine allocation problem is the key to build a private cloud environment. This paper presents a virtual machine mapping policy based on multi-resource load balancing. It uses the resource consumption of the running virtual machine and the self-adaptive weighted approach, which resolves the load balancing conflicts of each independent resource caused by different demand for resources of cloud applications. Meanwhile, it uses probability approach to ease the problem of load crowding in the concurrent users scene. The experiments and comparative analysis show that this policy achieves the better effect than existing approach.
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paper_title: A Load Balancing Scheme Using Federate Migration Based on Virtual Machines for Cloud Simulations

paper_content:

A maturing and promising technology, Cloud computing can benefit large-scale simulations by providing on-demand, anywhere simulation services to users. In order to enable multitask and multiuser simulation systems with Cloud computing, Cloud simulation platform (CSP) was proposed and developed. To use key techniques of Cloud computing such as virtualization to promote the running efficiency of large-scale military HLA systems, this paper proposes a new type of federate container, virtual machine (VM), and its dynamic migration algorithm considering both computation and communication cost. Experiments show that the migration scheme effectively improves the running efficiency of HLA system when the distributed system is not saturated.
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paper_title: Cloud Data Management

paper_content:

In practice, the design and architecture of a cloud varies among cloud providers. We present a generic evaluation framework for the performance, availability and reliability characteristics of various cloud platforms. We describe a generic benchmark architecture for cloud databases, specifically NoSQL database as a service. It measures the performance of replication delay and monetary cost. Service Level Agreements (SLA) represent the contract which captures the agreed upon guarantees between a service provider and its customers. The specifications of existing service level agreements (SLA) for cloud services are not designed to flexibly handle even relatively straightforward performance and technical requirements of consumer applications. We present a novel approach for SLA-based management of cloud-hosted databases from the consumer perspective and an end-to-end framework for consumer-centric SLA management of cloud-hosted databases. The framework facilitates adaptive and dynamic provisioning of the database tier of the software applications based on application-defined policies for satisfying their own SLA performance requirements, avoiding the cost of any SLA violation and controlling the monetary cost of the allocated computing resources. In this framework, the SLA of the consumer applications are declaratively defined in terms of goals which are subjected to a number of constraints that are specific to the application requirements. The framework continuously monitors the application-defined SLA and automatically triggers the execution of necessary corrective actions (scaling out/in the database tier) when required. The framework is database platform-agnostic, uses virtualization-based database replication mechanisms and requires zero source code changes of the cloud-hosted software applications.
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paper_title: Cloud computing: state-of-the-art and research challenges

paper_content:

Cloud computing has recently emerged as a new paradigm for hosting and delivering services over the Internet. Cloud computing is attractive to business owners as it eliminates the requirement for users to plan ahead for provisioning, and allows enterprises to start from the small and increase resources only when there is a rise in service demand. However, despite the fact that cloud computing offers huge opportunities to the IT industry, the development of cloud computing technology is currently at its infancy, with many issues still to be addressed. In this paper, we present a survey of cloud computing, highlighting its key concepts, architectural principles, state-of-the-art implementation as well as research challenges. The aim of this paper is to provide a better understanding of the design challenges of cloud computing and identify important research directions in this increasingly important area.
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paper_title: Above the Clouds: A Berkeley View of Cloud Computing

paper_content:

Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Developers with innovative ideas for new Internet services no longer require the large capital outlays in hardware to deploy their service or the human expense to operate it. They need not be concerned about overprovisioning for a service whose popularity does not meet their predictions, thus wasting costly resources, or underprovisioning for one that becomes wildly popular, thus missing potential customers and revenue. Moreover, companies with large batch-oriented tasks can get results as quickly as their programs can scale, since using 1000 servers for one hour costs no more than using one server for 1000 hours. This elasticity of resources, without paying a premium for large scale, is unprecedented in the history of IT. Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services. The services themselves have long been referred to as Software as a Service (SaaS). The datacenter hardware and software is what we will call a Cloud. When a Cloud is made available in a pay-as-you-go manner to the general public, we call it a Public Cloud; the service being sold is Utility Computing. We use the term Private Cloud to refer to internal datacenters of a business or other organization, not made available to the general public. Thus, Cloud Computing is the sum of SaaS and Utility Computing, but does not include Private Clouds. People can be users or providers of SaaS, or users or providers of Utility Computing. We focus on SaaS Providers (Cloud Users) and Cloud Providers, which have received less attention than SaaS Users. From a hardware point of view, three aspects are new in Cloud Computing.
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paper_title: A dynamic and integrated load-balancing scheduling algorithm for Cloud datacenters

paper_content:

One of the challenging scheduling problems in Cloud datacenters is to take the allocation and migration of reconfigurable virtual machines into consideration as well as the integrated features of hosting physical machines. We introduce a dynamic and integrated resource scheduling algorithm (DAIRS) for Cloud datacenters. Unlike traditional load-balance scheduling algorithms which consider only one factor such as the CPU load in physical servers, DAIRS treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop integrated measurement for the total imbalance level of a Cloud datacenter as well as the average imbalance level of each server. Simulation results show that DAIRS has good performance with regard to total imbalance level, average imbalance level of each server, as well as overall running time.
---
paper_title: Virtual machine mapping policy based on load balancing in private cloud environment

paper_content:

The virtual machine allocation problem is the key to build a private cloud environment. This paper presents a virtual machine mapping policy based on multi-resource load balancing. It uses the resource consumption of the running virtual machine and the self-adaptive weighted approach, which resolves the load balancing conflicts of each independent resource caused by different demand for resources of cloud applications. Meanwhile, it uses probability approach to ease the problem of load crowding in the concurrent users scene. The experiments and comparative analysis show that this policy achieves the better effect than existing approach.
---
paper_title: A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing

paper_content:

Virtual machine (VM) scheduling with load balancing in cloud computing aims to assign VMs to suitable servers and balance the resource usage among all of the servers. In an infrastructure-as-a-service framework, there will be dynamic input requests, where the system is in charge of creating VMs without considering what types of tasks run on them. Therefore, scheduling that focuses only on fixed task sets or that requires detailed task information is not suitable for this system. This paper combines ant colony optimization and particle swarm optimization to solve the VM scheduling problem, with the result being known as ant colony optimization with particle swarm (ACOPS). ACOPS uses historical information to predict the workload of new input requests to adapt to dynamic environments without additional task information. ACOPS also rejects requests that cannot be satisfied before scheduling to reduce the computing time of the scheduling procedure. Experimental results indicate that the proposed algorithm can keep the load balance in a dynamic environment and outperform other approaches.
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paper_title: Metaheuristics: From Design to Implementation

paper_content:

A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.
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paper_title: Virtual machine mapping policy based on load balancing in private cloud environment

paper_content:

The virtual machine allocation problem is the key to build a private cloud environment. This paper presents a virtual machine mapping policy based on multi-resource load balancing. It uses the resource consumption of the running virtual machine and the self-adaptive weighted approach, which resolves the load balancing conflicts of each independent resource caused by different demand for resources of cloud applications. Meanwhile, it uses probability approach to ease the problem of load crowding in the concurrent users scene. The experiments and comparative analysis show that this policy achieves the better effect than existing approach.
---
paper_title: A Load Balancing Scheme Using Federate Migration Based on Virtual Machines for Cloud Simulations

paper_content:

A maturing and promising technology, Cloud computing can benefit large-scale simulations by providing on-demand, anywhere simulation services to users. In order to enable multitask and multiuser simulation systems with Cloud computing, Cloud simulation platform (CSP) was proposed and developed. To use key techniques of Cloud computing such as virtualization to promote the running efficiency of large-scale military HLA systems, this paper proposes a new type of federate container, virtual machine (VM), and its dynamic migration algorithm considering both computation and communication cost. Experiments show that the migration scheme effectively improves the running efficiency of HLA system when the distributed system is not saturated.
---
paper_title: A distributed and collaborative dynamic load balancer for virtual machine

paper_content:

With the number of services using virtualization and clouds growing faster and faster, it is common to mutualize thousands of virtual machines within one distributed system. Consequently, the virtualized services, softwares, hardwares and infrastructures share the same physical resources, thus the performance of one depends of the resources usage of others. We propose a solution for vm load balancing (and rebalancing) based on the observation of the resources quota and the dynamic usage that leads to better balancing of resources. As it is not possible to have a single scheduler for the whole cloud and to avoid a single point of failure, our scheduler uses distributed and collaborative scheduling agents. We present scenarios simulating various cloud resources and vm usage experimented on our testbed p2p architecture.
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paper_title: Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers

paper_content:

In the past few years, we have witnessed the proliferation of a heterogeneous ecosystem of cloud providers, each one with a different infrastructure offer and pricing policy. We explore this heterogeneity in a novel cloud brokering approach that optimizes placement of virtual infrastructures across multiple clouds and also abstracts the deployment and management of infrastructure components in these clouds. The feasibility of our approach is evaluated in a high throughput computing cluster case study. Experimental results confirm that multi-cloud deployment provides better performance and lower costs compared to the usage of a single cloud only.
---
paper_title: A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment

paper_content:

The current virtual machine(VM) resources scheduling in cloud computing environment mainly considers the current state of the system but seldom considers system variation and historical data, which always leads to load imbalance of the system. In view of the load balancing problem in VM resources scheduling, this paper presents a scheduling strategy on load balancing of VM resources based on genetic algorithm. According to historical data and current state of the system and through genetic algorithm, this strategy computes ahead the influence it will have on the system after the deployment of the needed VM resources and then chooses the least-affective solution, through which it achieves the best load balancing and reduces or avoids dynamic migration. This strategy solves the problem of load imbalance and high migration cost by traditional algorithms after scheduling. Experimental results prove that this method is able to realize load balancing and reasonable resources utilization both when system load is stable and variant.
---
paper_title: An online load balancing scheduling algorithm for cloud data centers considering real-time multi-dimensional resource

paper_content:

In general, load-balance scheduling is NP-hard problem as proved in many open literatures. We introduce an online load balancing resource scheduling algorithm (OLRSA) for Cloud datacenters considering real-time and multi-dimensional resources. Unlike traditional load balance scheduling algorithms which often consider only one factor such as the CPU load in physical servers, OLRSA treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop and apply integrated measurement for each server and a Cloud datacenter. Simulation results show that OLRSA has better performance than a few related load-balancing algorithms with regard to total imbalance level, makespan, as well as overall load efficiency.
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paper_title: Performance vector-based algorithm for virtual machine deployment in infrastructure clouds

paper_content:

Regarding the virtual machine deployment issues in cloud computing,the Performance Matching-Load Balancing(PM-LB) algorithm of virtual machine deployment was proposed.With performance vector,the performance standardization of virtual infrastructure was described.The matching vector was obtained by calculating the relative vector distance of virtual machine and the servers,then a comprehensive analysis of matching vector and load balancing vector was done to get the deployment result.The results of simulation in CloudSim environment prove that using the proposed algorithm can obtain better load-balancing performance and higher resource utilization.
---
paper_title: Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers

paper_content:

In the past few years, we have witnessed the proliferation of a heterogeneous ecosystem of cloud providers, each one with a different infrastructure offer and pricing policy. We explore this heterogeneity in a novel cloud brokering approach that optimizes placement of virtual infrastructures across multiple clouds and also abstracts the deployment and management of infrastructure components in these clouds. The feasibility of our approach is evaluated in a high throughput computing cluster case study. Experimental results confirm that multi-cloud deployment provides better performance and lower costs compared to the usage of a single cloud only.
---
paper_title: A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing

paper_content:

Virtual machine (VM) scheduling with load balancing in cloud computing aims to assign VMs to suitable servers and balance the resource usage among all of the servers. In an infrastructure-as-a-service framework, there will be dynamic input requests, where the system is in charge of creating VMs without considering what types of tasks run on them. Therefore, scheduling that focuses only on fixed task sets or that requires detailed task information is not suitable for this system. This paper combines ant colony optimization and particle swarm optimization to solve the VM scheduling problem, with the result being known as ant colony optimization with particle swarm (ACOPS). ACOPS uses historical information to predict the workload of new input requests to adapt to dynamic environments without additional task information. ACOPS also rejects requests that cannot be satisfied before scheduling to reduce the computing time of the scheduling procedure. Experimental results indicate that the proposed algorithm can keep the load balance in a dynamic environment and outperform other approaches.
---
paper_title: Energy Efficient Multi Dimensional Host Load Aware Algorithm for Virtual Machine Placement and Optimization in Cloud Environment

paper_content:

The effectiveness and elasticity of virtual machine placement has become a main concern in modern cloud computing environment. Mapping the virtual machines to the physical machines cluster is called the VM placement. In this paper we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We used two different techniques, first initial VM packing is done by checking the load of the physical host and the user constraints of the VMs. Second optimization of placed VMs is done by using a hybrid genetic algorithm based on fitness function. The presented algorithm is implemented in JAVA Net beans IDE, and Clouds simulator has been used for simulation to assess the execution and performance of our heuristics by comparison with algorithms first fit, best fit and round robin. The performance of the proposed algorithm was examined from both users and service provider’s perception. The simulation results show that our proposed algorithm uses the less number of physical servers for placing a certain number of VMs which helps to improve the resource utilization rate. The response time of our algorithm is little bit more than the first fit algorithm because of its nature of allocating VMs is based on the user constraints and past usage history of the VMs. Elevated SLA satisfaction rate and inferior load imbalance rate was observed in results. Since we used a modified version of hybrid genetic algorithm for load optimization the percentage of VM migrations had been decreased through which we can achieve the better results for load balancing along with cost reduction. The results also show that our hybrid genetic based multi dimensional host load aware and user constraints based algorithm is applicable, valuable and reliable for implementation in real data center environments.
---
paper_title: A dynamic and integrated load-balancing scheduling algorithm for Cloud datacenters

paper_content:

One of the challenging scheduling problems in Cloud datacenters is to take the allocation and migration of reconfigurable virtual machines into consideration as well as the integrated features of hosting physical machines. We introduce a dynamic and integrated resource scheduling algorithm (DAIRS) for Cloud datacenters. Unlike traditional load-balance scheduling algorithms which consider only one factor such as the CPU load in physical servers, DAIRS treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop integrated measurement for the total imbalance level of a Cloud datacenter as well as the average imbalance level of each server. Simulation results show that DAIRS has good performance with regard to total imbalance level, average imbalance level of each server, as well as overall running time.
---
paper_title: Prepartition: A new paradigm for the load balance of virtual machine reservations in data centers

paper_content:

It is significant to apply load-balancing strategy to improve the performance and reliability of resource in data centers. One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines (VMs) as well as the integrated features of hosting physical machines (PMs) into consideration. In the reservation model, workload of data centers has fixed process interval characteristics. In general, load-balance scheduling is NP-hard problem as proved in many open literatures. Traditionally, for offline load balance without migration, one of the best approaches is LPT (Longest Process Time first), which is well known to have approximation ratio 4/3. With virtualization, reactive (post) migration of VMs after allocation is one popular way for load balance and traffic consolidation. However, reactive migration has difficulty to reach predefined load balance objectives, and may cause interruption and instability of service and other associated costs. In view of this, we propose a new paradigm-Prepartition: it proactively sets process-time bound for each request on each PM and prepares in advance to migrate VMs to achieve the predefined balance goal. Prepartition can reduce process time by preparing VM migration in advance and therefore reduce instability and achieve better load balance as desired. Trace-driven and synthetic simulation results show that Prepartition has 10%-20% better performance than the well known load balancing algorithms with regard to average CPU utilization, makespan as well as capacity makespan.
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paper_title: Performance evaluation of web servers using central load balancing policy over virtual machines on cloud

paper_content:

Cloud Computing adds more power to the existing Internet technologies. Virtualization harnesses the power of the existing infrastructure and resources. With virtualization we can simultaneously run multiple instances of different commodity operating systems. Since we have limited processors and jobs work in concurrent fashion, overload situations can occur. Things become even more challenging in distributed environment. We propose Central Load Balancing Policy for Virtual Machines (CLBVM) to balance the load evenly in a distributed virtual machine/cloud computing environment. This work tries to compare the performance of web servers based on our CLBVM policy and independent virtual machine(VM) running on a single physical server using Xen Virtualizaion. The paper discusses the efficacy and feasibility of using this kind of policy for overall performance improvement.
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paper_title: A Quadratic Equilibrium Entropy Based Virtual Machine Load Balance Evaluation Algorithm

paper_content:

Aiming at virtual machine load balance evaluation, this paper proposes an evaluation algorithm based on quadratic equilibrium entropy. Firstly, we propose the computational methods for linear equilibrium and quadratic equilibrium entropy. Secondly, we analyze the physical meanings of linear equilibrium and quadratic equilibrium, and analyze their application method in virtual machine load balance. Finally, we prove our scheme by giving experimental results.
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paper_title: A distributed and collaborative dynamic load balancer for virtual machine

paper_content:

With the number of services using virtualization and clouds growing faster and faster, it is common to mutualize thousands of virtual machines within one distributed system. Consequently, the virtualized services, softwares, hardwares and infrastructures share the same physical resources, thus the performance of one depends of the resources usage of others. We propose a solution for vm load balancing (and rebalancing) based on the observation of the resources quota and the dynamic usage that leads to better balancing of resources. As it is not possible to have a single scheduler for the whole cloud and to avoid a single point of failure, our scheduler uses distributed and collaborative scheduling agents. We present scenarios simulating various cloud resources and vm usage experimented on our testbed p2p architecture.
---
paper_title: Server-storage virtualization: integration and load balancing in data centers

paper_content:

We describe the design of an agile data center with integrated server and storage virtualization technologies. Such data centers form a key building block for new cloud computing architectures. We also show how to leverage this integrated agility for non-disruptive load balancing in data centers across multiple resource layers - servers, switches, and storage. We propose a novel load balancing algorithm called VectorDot for handling the hierarchical and multi-dimensional resource constraints in such systems. The algorithm, inspired by the successful Toyoda method for multi-dimensional knapsacks, is the first of its kind. We evaluate our system on a range of synthetic and real data center testbeds comprising of VMware ESX servers, IBM SAN Volume Controller, Cisco and Brocade switches. Experiments under varied conditions demonstrate the end-to-end validity of our system and the ability of VectorDot to efficiently remove overloads on server, switch and storage nodes.
---
paper_title: Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers

paper_content:

In the past few years, we have witnessed the proliferation of a heterogeneous ecosystem of cloud providers, each one with a different infrastructure offer and pricing policy. We explore this heterogeneity in a novel cloud brokering approach that optimizes placement of virtual infrastructures across multiple clouds and also abstracts the deployment and management of infrastructure components in these clouds. The feasibility of our approach is evaluated in a high throughput computing cluster case study. Experimental results confirm that multi-cloud deployment provides better performance and lower costs compared to the usage of a single cloud only.
---
paper_title: A dynamic and integrated load-balancing scheduling algorithm for Cloud datacenters

paper_content:

One of the challenging scheduling problems in Cloud datacenters is to take the allocation and migration of reconfigurable virtual machines into consideration as well as the integrated features of hosting physical machines. We introduce a dynamic and integrated resource scheduling algorithm (DAIRS) for Cloud datacenters. Unlike traditional load-balance scheduling algorithms which consider only one factor such as the CPU load in physical servers, DAIRS treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop integrated measurement for the total imbalance level of a Cloud datacenter as well as the average imbalance level of each server. Simulation results show that DAIRS has good performance with regard to total imbalance level, average imbalance level of each server, as well as overall running time.
---
paper_title: Open-Source Simulators for Cloud Computing: Comparative Study and Challenging Issues

paper_content:

Abstract Resource scheduling in infrastructure as a service (IaaS) is one of the keys for large-scale Cloud applications. Extensive research on all issues in real environment is extremely difficult because it requires developers to consider network infrastructure and the environment, which may be beyond the control. In addition, the network conditions cannot be controlled or predicted. Performance evaluations of workload models and Cloud provisioning algorithms in a repeatable manner under different configurations are difficult. Therefore, simulators are developed. To understand and apply better the state-of-the-art of Cloud computing simulators, and to improve them, we study four known open-source simulators. They are compared in terms of architecture, modeling elements, simulation process, performance metrics and scalability in performance. Finally, a few challenging issues as future research trends are outlined.
---
paper_title: Prepartition: A new paradigm for the load balance of virtual machine reservations in data centers

paper_content:

It is significant to apply load-balancing strategy to improve the performance and reliability of resource in data centers. One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines (VMs) as well as the integrated features of hosting physical machines (PMs) into consideration. In the reservation model, workload of data centers has fixed process interval characteristics. In general, load-balance scheduling is NP-hard problem as proved in many open literatures. Traditionally, for offline load balance without migration, one of the best approaches is LPT (Longest Process Time first), which is well known to have approximation ratio 4/3. With virtualization, reactive (post) migration of VMs after allocation is one popular way for load balance and traffic consolidation. However, reactive migration has difficulty to reach predefined load balance objectives, and may cause interruption and instability of service and other associated costs. In view of this, we propose a new paradigm-Prepartition: it proactively sets process-time bound for each request on each PM and prepares in advance to migrate VMs to achieve the predefined balance goal. Prepartition can reduce process time by preparing VM migration in advance and therefore reduce instability and achieve better load balance as desired. Trace-driven and synthetic simulation results show that Prepartition has 10%-20% better performance than the well known load balancing algorithms with regard to average CPU utilization, makespan as well as capacity makespan.
---
paper_title: FlexCloud: A Flexible and Extendible Simulator for Performance Evaluation of Virtual Machine Allocation

paper_content:

Cloud Data centers aim to provide reliable, sustainable and scalable services for all kinds of applications. Resource scheduling is one of keys to cloud services. To model and evaluate different scheduling policies and algorithms, we propose FlexCloud, a flexible and scalable simulator that enables users to simulate the process of initializing cloud data centers, allocating virtual machine requests and providing performance evaluation for various scheduling algorithms. FlexCloud can be run on a single computer with JVM to simulate large scale cloud environments with focus on infrastructure as a service; adopts agile design patterns to assure the flexibility and extensibility; models virtual machine migrations which is lack in the existing tools; provides user-friendly interfaces for customized configurations and replaying. Comparing to existing simulators, FlexCloud has combining features for supporting public cloud providers, load-balance and energy-efficiency scheduling. FlexCloud has advantage in computing time and memory consumption to support large-scale simulations. The detailed design of FlexCloud is introduced and performance evaluation is provided.
---
paper_title: A Toolkit for Modeling and Simulation of Real-Time Virtual Machine Allocation in a Cloud Data Center

paper_content:

Resource scheduling in infrastructure as a service (IaaS) is one of the keys for large-scale Cloud applications. Extensive research on all issues in real environment is extremely difficult because it requires developers to consider network infrastructure and the environment, which may be beyond the control. In addition, the network conditions cannot be predicted or controlled. Therefore, performance evaluation of workload models and Cloud provisioning algorithms in a repeatable manner under different configurations and requirements is difficult. There is still lack of tools that enable developers to compare different resource scheduling algorithms in IaaS regarding both computing servers and user workloads. To fill this gap in tools for evaluation and modeling of Cloud environments and applications, we propose CloudSched. CloudSched can help developers identify and explore appropriate solutions considering different resource scheduling algorithms. Unlike traditional scheduling algorithms considering only one factor such as CPU, which can cause hotspots or bottlenecks in many cases, CloudSched treats multidimensional resource such as CPU, memory and network bandwidth integrated for both physical machines and virtual machines (VMs) for different scheduling objectives (algorithms). In this paper, two existing simulation systems at application level for Cloud computing are studied, a novel lightweight simulation system is proposed for real-time VM scheduling in Cloud data centers, and results by applying the proposed simulation system are analyzed and discussed.
---
paper_title: A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing

paper_content:

Virtual machine (VM) scheduling with load balancing in cloud computing aims to assign VMs to suitable servers and balance the resource usage among all of the servers. In an infrastructure-as-a-service framework, there will be dynamic input requests, where the system is in charge of creating VMs without considering what types of tasks run on them. Therefore, scheduling that focuses only on fixed task sets or that requires detailed task information is not suitable for this system. This paper combines ant colony optimization and particle swarm optimization to solve the VM scheduling problem, with the result being known as ant colony optimization with particle swarm (ACOPS). ACOPS uses historical information to predict the workload of new input requests to adapt to dynamic environments without additional task information. ACOPS also rejects requests that cannot be satisfied before scheduling to reduce the computing time of the scheduling procedure. Experimental results indicate that the proposed algorithm can keep the load balance in a dynamic environment and outperform other approaches.
---
paper_title: Energy Efficient Multi Dimensional Host Load Aware Algorithm for Virtual Machine Placement and Optimization in Cloud Environment

paper_content:

The effectiveness and elasticity of virtual machine placement has become a main concern in modern cloud computing environment. Mapping the virtual machines to the physical machines cluster is called the VM placement. In this paper we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We used two different techniques, first initial VM packing is done by checking the load of the physical host and the user constraints of the VMs. Second optimization of placed VMs is done by using a hybrid genetic algorithm based on fitness function. The presented algorithm is implemented in JAVA Net beans IDE, and Clouds simulator has been used for simulation to assess the execution and performance of our heuristics by comparison with algorithms first fit, best fit and round robin. The performance of the proposed algorithm was examined from both users and service provider’s perception. The simulation results show that our proposed algorithm uses the less number of physical servers for placing a certain number of VMs which helps to improve the resource utilization rate. The response time of our algorithm is little bit more than the first fit algorithm because of its nature of allocating VMs is based on the user constraints and past usage history of the VMs. Elevated SLA satisfaction rate and inferior load imbalance rate was observed in results. Since we used a modified version of hybrid genetic algorithm for load optimization the percentage of VM migrations had been decreased through which we can achieve the better results for load balancing along with cost reduction. The results also show that our hybrid genetic based multi dimensional host load aware and user constraints based algorithm is applicable, valuable and reliable for implementation in real data center environments.
---
paper_title: A dynamic and integrated load-balancing scheduling algorithm for Cloud datacenters

paper_content:

One of the challenging scheduling problems in Cloud datacenters is to take the allocation and migration of reconfigurable virtual machines into consideration as well as the integrated features of hosting physical machines. We introduce a dynamic and integrated resource scheduling algorithm (DAIRS) for Cloud datacenters. Unlike traditional load-balance scheduling algorithms which consider only one factor such as the CPU load in physical servers, DAIRS treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop integrated measurement for the total imbalance level of a Cloud datacenter as well as the average imbalance level of each server. Simulation results show that DAIRS has good performance with regard to total imbalance level, average imbalance level of each server, as well as overall running time.
---
paper_title: Prepartition: A new paradigm for the load balance of virtual machine reservations in data centers

paper_content:

It is significant to apply load-balancing strategy to improve the performance and reliability of resource in data centers. One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines (VMs) as well as the integrated features of hosting physical machines (PMs) into consideration. In the reservation model, workload of data centers has fixed process interval characteristics. In general, load-balance scheduling is NP-hard problem as proved in many open literatures. Traditionally, for offline load balance without migration, one of the best approaches is LPT (Longest Process Time first), which is well known to have approximation ratio 4/3. With virtualization, reactive (post) migration of VMs after allocation is one popular way for load balance and traffic consolidation. However, reactive migration has difficulty to reach predefined load balance objectives, and may cause interruption and instability of service and other associated costs. In view of this, we propose a new paradigm-Prepartition: it proactively sets process-time bound for each request on each PM and prepares in advance to migrate VMs to achieve the predefined balance goal. Prepartition can reduce process time by preparing VM migration in advance and therefore reduce instability and achieve better load balance as desired. Trace-driven and synthetic simulation results show that Prepartition has 10%-20% better performance than the well known load balancing algorithms with regard to average CPU utilization, makespan as well as capacity makespan.
---
paper_title: A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment

paper_content:

The current virtual machine(VM) resources scheduling in cloud computing environment mainly considers the current state of the system but seldom considers system variation and historical data, which always leads to load imbalance of the system. In view of the load balancing problem in VM resources scheduling, this paper presents a scheduling strategy on load balancing of VM resources based on genetic algorithm. According to historical data and current state of the system and through genetic algorithm, this strategy computes ahead the influence it will have on the system after the deployment of the needed VM resources and then chooses the least-affective solution, through which it achieves the best load balancing and reduces or avoids dynamic migration. This strategy solves the problem of load imbalance and high migration cost by traditional algorithms after scheduling. Experimental results prove that this method is able to realize load balancing and reasonable resources utilization both when system load is stable and variant.
---
paper_title: A Load Balancing Scheme Using Federate Migration Based on Virtual Machines for Cloud Simulations

paper_content:

A maturing and promising technology, Cloud computing can benefit large-scale simulations by providing on-demand, anywhere simulation services to users. In order to enable multitask and multiuser simulation systems with Cloud computing, Cloud simulation platform (CSP) was proposed and developed. To use key techniques of Cloud computing such as virtualization to promote the running efficiency of large-scale military HLA systems, this paper proposes a new type of federate container, virtual machine (VM), and its dynamic migration algorithm considering both computation and communication cost. Experiments show that the migration scheme effectively improves the running efficiency of HLA system when the distributed system is not saturated.
---
paper_title: Virtual machine mapping policy based on load balancing in private cloud environment

paper_content:

The virtual machine allocation problem is the key to build a private cloud environment. This paper presents a virtual machine mapping policy based on multi-resource load balancing. It uses the resource consumption of the running virtual machine and the self-adaptive weighted approach, which resolves the load balancing conflicts of each independent resource caused by different demand for resources of cloud applications. Meanwhile, it uses probability approach to ease the problem of load crowding in the concurrent users scene. The experiments and comparative analysis show that this policy achieves the better effect than existing approach.
---
paper_title: Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers

paper_content:

In the past few years, we have witnessed the proliferation of a heterogeneous ecosystem of cloud providers, each one with a different infrastructure offer and pricing policy. We explore this heterogeneity in a novel cloud brokering approach that optimizes placement of virtual infrastructures across multiple clouds and also abstracts the deployment and management of infrastructure components in these clouds. The feasibility of our approach is evaluated in a high throughput computing cluster case study. Experimental results confirm that multi-cloud deployment provides better performance and lower costs compared to the usage of a single cloud only.
---
paper_title: An Optimized Control Strategy for Load Balancing Based on Live Migration of Virtual Machine

paper_content:

The migration technology of virtual machine has received extensive attention in load balancing. In this paper, we purpose an optimized control strategy which combines multi-strategy mechanism with the prediction mechanism. According to the weighted average of the CPU, memory, I/O and network bandwidth utilization, we divide the hosts into four status domains. The hosts within different load status domain adopt different migration strategy, which considers migration timing, migration candidate VM and migration destination. Through this strategy, it reduces the number of the overloaded hosts, avoids instantaneous peak problem caused by the migration of virtual machines, solves the imbalance problem and the high-cost problem in tradition scheduling algorithm of migration. Experimental results demonstrate that this strategy is able to reduce the migration times and improve system performance.
---
paper_title: AR model prediction of time series with trends and seasonalities: A contrast with Box-Jenkins modeling

paper_content:

A "long autoregressive (AR) model alternative to the classical Box-Jenkins ARIMA model method of modeling time series with trend and seasonality characteristics is considered. Superior forecast performance is demonstrated by our long AR model method on the Box-Jenkins Series G airline passenger data. The difference in performance is accounted for by the relative underparameterization of the Box-Jenkins method. A Householder transformation-Akaike AIC criterion method is employed for determining the best data transformed, detrended-deseasonalized stationary residuals-AR modeled time series.
---
paper_title: Performance evaluation of web servers using central load balancing policy over virtual machines on cloud

paper_content:

Cloud Computing adds more power to the existing Internet technologies. Virtualization harnesses the power of the existing infrastructure and resources. With virtualization we can simultaneously run multiple instances of different commodity operating systems. Since we have limited processors and jobs work in concurrent fashion, overload situations can occur. Things become even more challenging in distributed environment. We propose Central Load Balancing Policy for Virtual Machines (CLBVM) to balance the load evenly in a distributed virtual machine/cloud computing environment. This work tries to compare the performance of web servers based on our CLBVM policy and independent virtual machine(VM) running on a single physical server using Xen Virtualizaion. The paper discusses the efficacy and feasibility of using this kind of policy for overall performance improvement.
---
paper_title: A distributed and collaborative dynamic load balancer for virtual machine

paper_content:

With the number of services using virtualization and clouds growing faster and faster, it is common to mutualize thousands of virtual machines within one distributed system. Consequently, the virtualized services, softwares, hardwares and infrastructures share the same physical resources, thus the performance of one depends of the resources usage of others. We propose a solution for vm load balancing (and rebalancing) based on the observation of the resources quota and the dynamic usage that leads to better balancing of resources. As it is not possible to have a single scheduler for the whole cloud and to avoid a single point of failure, our scheduler uses distributed and collaborative scheduling agents. We present scenarios simulating various cloud resources and vm usage experimented on our testbed p2p architecture.
---
paper_title: A dynamic and integrated load-balancing scheduling algorithm for Cloud datacenters

paper_content:

One of the challenging scheduling problems in Cloud datacenters is to take the allocation and migration of reconfigurable virtual machines into consideration as well as the integrated features of hosting physical machines. We introduce a dynamic and integrated resource scheduling algorithm (DAIRS) for Cloud datacenters. Unlike traditional load-balance scheduling algorithms which consider only one factor such as the CPU load in physical servers, DAIRS treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop integrated measurement for the total imbalance level of a Cloud datacenter as well as the average imbalance level of each server. Simulation results show that DAIRS has good performance with regard to total imbalance level, average imbalance level of each server, as well as overall running time.
---
paper_title: Bounds on Multiprocessing Timing Anomalies

paper_content:

An apparatus for generating sparks over a selected area to be used for theatrical effects. Metal wire having a diameter in the range of 0.020-0.125 inches is provided by two, independent supply sources. Each wire supply source is coupled to a wire guide which imposes synchronous, linear movement to each wire source at a selected rate. Each wire source is coupled to a tip assembly which places the terminus of each wire source adjacent one another. The positive and negative electrodes of a direct current power source are electrically connected to a respective terminus of each of the pair of wire sources, the output of the direct current power source is amplified to voltage sufficient to atomize the wire when the power source is short circuited. The atomization of the wire results in the production of heated, metallic particles simulating generated sparks. A source of compressed air is disposed adjacent the point of atomization. The atomized particles are disseminated across an area determined by the force imposed thereon by the compressed air.
---
paper_title: Prepartition: A new paradigm for the load balance of virtual machine reservations in data centers

paper_content:

It is significant to apply load-balancing strategy to improve the performance and reliability of resource in data centers. One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines (VMs) as well as the integrated features of hosting physical machines (PMs) into consideration. In the reservation model, workload of data centers has fixed process interval characteristics. In general, load-balance scheduling is NP-hard problem as proved in many open literatures. Traditionally, for offline load balance without migration, one of the best approaches is LPT (Longest Process Time first), which is well known to have approximation ratio 4/3. With virtualization, reactive (post) migration of VMs after allocation is one popular way for load balance and traffic consolidation. However, reactive migration has difficulty to reach predefined load balance objectives, and may cause interruption and instability of service and other associated costs. In view of this, we propose a new paradigm-Prepartition: it proactively sets process-time bound for each request on each PM and prepares in advance to migrate VMs to achieve the predefined balance goal. Prepartition can reduce process time by preparing VM migration in advance and therefore reduce instability and achieve better load balance as desired. Trace-driven and synthetic simulation results show that Prepartition has 10%-20% better performance than the well known load balancing algorithms with regard to average CPU utilization, makespan as well as capacity makespan.
---
paper_title: Energy Efficient Multi Dimensional Host Load Aware Algorithm for Virtual Machine Placement and Optimization in Cloud Environment

paper_content:

The effectiveness and elasticity of virtual machine placement has become a main concern in modern cloud computing environment. Mapping the virtual machines to the physical machines cluster is called the VM placement. In this paper we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We used two different techniques, first initial VM packing is done by checking the load of the physical host and the user constraints of the VMs. Second optimization of placed VMs is done by using a hybrid genetic algorithm based on fitness function. The presented algorithm is implemented in JAVA Net beans IDE, and Clouds simulator has been used for simulation to assess the execution and performance of our heuristics by comparison with algorithms first fit, best fit and round robin. The performance of the proposed algorithm was examined from both users and service provider’s perception. The simulation results show that our proposed algorithm uses the less number of physical servers for placing a certain number of VMs which helps to improve the resource utilization rate. The response time of our algorithm is little bit more than the first fit algorithm because of its nature of allocating VMs is based on the user constraints and past usage history of the VMs. Elevated SLA satisfaction rate and inferior load imbalance rate was observed in results. Since we used a modified version of hybrid genetic algorithm for load optimization the percentage of VM migrations had been decreased through which we can achieve the better results for load balancing along with cost reduction. The results also show that our hybrid genetic based multi dimensional host load aware and user constraints based algorithm is applicable, valuable and reliable for implementation in real data center environments.
---
paper_title: Prepartition: A new paradigm for the load balance of virtual machine reservations in data centers

paper_content:

It is significant to apply load-balancing strategy to improve the performance and reliability of resource in data centers. One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines (VMs) as well as the integrated features of hosting physical machines (PMs) into consideration. In the reservation model, workload of data centers has fixed process interval characteristics. In general, load-balance scheduling is NP-hard problem as proved in many open literatures. Traditionally, for offline load balance without migration, one of the best approaches is LPT (Longest Process Time first), which is well known to have approximation ratio 4/3. With virtualization, reactive (post) migration of VMs after allocation is one popular way for load balance and traffic consolidation. However, reactive migration has difficulty to reach predefined load balance objectives, and may cause interruption and instability of service and other associated costs. In view of this, we propose a new paradigm-Prepartition: it proactively sets process-time bound for each request on each PM and prepares in advance to migrate VMs to achieve the predefined balance goal. Prepartition can reduce process time by preparing VM migration in advance and therefore reduce instability and achieve better load balance as desired. Trace-driven and synthetic simulation results show that Prepartition has 10%-20% better performance than the well known load balancing algorithms with regard to average CPU utilization, makespan as well as capacity makespan.
---
paper_title: Virtual machine mapping policy based on load balancing in private cloud environment

paper_content:

The virtual machine allocation problem is the key to build a private cloud environment. This paper presents a virtual machine mapping policy based on multi-resource load balancing. It uses the resource consumption of the running virtual machine and the self-adaptive weighted approach, which resolves the load balancing conflicts of each independent resource caused by different demand for resources of cloud applications. Meanwhile, it uses probability approach to ease the problem of load crowding in the concurrent users scene. The experiments and comparative analysis show that this policy achieves the better effect than existing approach.
---
paper_title: Energy Efficient Multi Dimensional Host Load Aware Algorithm for Virtual Machine Placement and Optimization in Cloud Environment

paper_content:

The effectiveness and elasticity of virtual machine placement has become a main concern in modern cloud computing environment. Mapping the virtual machines to the physical machines cluster is called the VM placement. In this paper we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We used two different techniques, first initial VM packing is done by checking the load of the physical host and the user constraints of the VMs. Second optimization of placed VMs is done by using a hybrid genetic algorithm based on fitness function. The presented algorithm is implemented in JAVA Net beans IDE, and Clouds simulator has been used for simulation to assess the execution and performance of our heuristics by comparison with algorithms first fit, best fit and round robin. The performance of the proposed algorithm was examined from both users and service provider’s perception. The simulation results show that our proposed algorithm uses the less number of physical servers for placing a certain number of VMs which helps to improve the resource utilization rate. The response time of our algorithm is little bit more than the first fit algorithm because of its nature of allocating VMs is based on the user constraints and past usage history of the VMs. Elevated SLA satisfaction rate and inferior load imbalance rate was observed in results. Since we used a modified version of hybrid genetic algorithm for load optimization the percentage of VM migrations had been decreased through which we can achieve the better results for load balancing along with cost reduction. The results also show that our hybrid genetic based multi dimensional host load aware and user constraints based algorithm is applicable, valuable and reliable for implementation in real data center environments.
---
paper_title: A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment

paper_content:

The current virtual machine(VM) resources scheduling in cloud computing environment mainly considers the current state of the system but seldom considers system variation and historical data, which always leads to load imbalance of the system. In view of the load balancing problem in VM resources scheduling, this paper presents a scheduling strategy on load balancing of VM resources based on genetic algorithm. According to historical data and current state of the system and through genetic algorithm, this strategy computes ahead the influence it will have on the system after the deployment of the needed VM resources and then chooses the least-affective solution, through which it achieves the best load balancing and reduces or avoids dynamic migration. This strategy solves the problem of load imbalance and high migration cost by traditional algorithms after scheduling. Experimental results prove that this method is able to realize load balancing and reasonable resources utilization both when system load is stable and variant.
---
paper_title: Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers

paper_content:

In the past few years, we have witnessed the proliferation of a heterogeneous ecosystem of cloud providers, each one with a different infrastructure offer and pricing policy. We explore this heterogeneity in a novel cloud brokering approach that optimizes placement of virtual infrastructures across multiple clouds and also abstracts the deployment and management of infrastructure components in these clouds. The feasibility of our approach is evaluated in a high throughput computing cluster case study. Experimental results confirm that multi-cloud deployment provides better performance and lower costs compared to the usage of a single cloud only.
---
paper_title: A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing

paper_content:

Virtual machine (VM) scheduling with load balancing in cloud computing aims to assign VMs to suitable servers and balance the resource usage among all of the servers. In an infrastructure-as-a-service framework, there will be dynamic input requests, where the system is in charge of creating VMs without considering what types of tasks run on them. Therefore, scheduling that focuses only on fixed task sets or that requires detailed task information is not suitable for this system. This paper combines ant colony optimization and particle swarm optimization to solve the VM scheduling problem, with the result being known as ant colony optimization with particle swarm (ACOPS). ACOPS uses historical information to predict the workload of new input requests to adapt to dynamic environments without additional task information. ACOPS also rejects requests that cannot be satisfied before scheduling to reduce the computing time of the scheduling procedure. Experimental results indicate that the proposed algorithm can keep the load balance in a dynamic environment and outperform other approaches.
---

Title: A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing
Section 1: INTRODUCTION
Description 1: This section provides an overview of the significance of load balancing in traditional and cloud data centers and outlines the key contributions and structure of the paper.

Section 2: Related Work
Description 2: This section reviews existing literature and survey papers related to VM load balancing, highlighting their focus areas and limitations.

Section 3: Related Technology
Description 3: This section introduces essential technologies related to load balancing, including virtualization, VM migration, and VM consolidation.

Section 4: Scenario
Description 4: This section outlines different scenarios for VM load balancing algorithms, including public, private, and hybrid cloud environments, and discusses constraints and characteristics specific to each scenario.

Section 5: VM LOAD BALANCING ALGORITHM MODELING IN CLOUDS
Description 5: This section discusses the design considerations for VM load balancing algorithms, including VM resource type, VM type uniformity, allocation dynamicity, optimization strategy, and scheduling process.

Section 6: LOAD BALANCING SCHEDULING METRICS COMPARISON
Description 6: This section lists and describes various metrics used to evaluate VM load balancing algorithms, such as load variance, makespan, and SLA violations.

Section 7: PERFORMANCE EVALUATION APPROACHES
Description 7: This section presents different platforms and simulation toolkits used for evaluating the performance of VM load balancing algorithms, providing examples of experimental configurations and results.

Section 8: ALGORITHMS COMPARISON
Description 8: This section provides detailed descriptions and comparisons of several specific VM load balancing algorithms, highlighting their strengths and weaknesses.

Section 9: CHALLENGES AND FUTURE DIRECTIONS
Description 9: This section discusses the challenges faced by current VM load balancing algorithms and proposes future research directions to address these challenges.