A Survey on Resource Allocation and Monitoring in Cloud Computing
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paper_title: Cloud Computing: Issues and Challenges

paper_content:

Many believe that Cloud will reshape the entire ICT industry as a revolution. In this paper, we aim to pinpoint the challenges and issues of Cloud computing. We first discuss two related computing paradigms - Service-Oriented Computing and Grid computing, and their relationships with Cloud computing We then identify several challenges from the Cloud computing adoption perspective. Last, we will highlight the Cloud interoperability issue that deserves substantial further research and development.
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paper_title: Resource provisioning for cloud computing

paper_content:

In resource provisioning for cloud computing, an important issue is how resources may be allocated to an application mix such that the service level agreements (SLAs) of all applications are met. A performance model with two interactive job classes is used to determine the smallest number of servers required to meet the SLAs of both classes. For each class, the SLA is specified by the relationship: Prob [response time ≤ x] ≥ y. Two server allocation strategies are considered: shared allocation (SA) and dedicated allocation (DA). For the case of FCFS scheduling, analytic results for response time distribution are used to develop a heuristic algorithm that determines an allocation strategy (SA or DA) that requires the smallest number of servers. The effectiveness of this algorithm is evaluated over a range of operating conditions. The performance of SA with non-FCFS scheduling is also investigated. Among the scheduling disciplines considered, a new discipline called probability dependent priority is found to have the best performance in terms of requiring the smallest number of servers.
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paper_title: An architecture model of management and monitoring on Cloud services resources

paper_content:

As a new computing paradigm, Cloud computing has been advocated by IT industry and academia. It can provide lots of services, however, how to manage these services and guarantee the quality of service, has become one of the key factors for the development of Cloud computing. This paper proposes a hierarchical architecture model of Cloud services resources management and monitoring by integrating the power of Service-Oriented Architecture and the concept of IT service management. The architecture model uses service monitoring and process of IT service management to realize management of Cloud services resources, and then provides foundation for technology to ensure the quality of service of Cloud services. Therefore, it could be used as a reference architecture model of management and monitoring on Cloud services resources.
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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: NIST Cloud Computing Reference Architecture

paper_content:

This paper presents the first version of the NIST Cloud Computing Reference Architecture (RA). This is a vendor neutral conceptual model that concentrates on the role and interactions of the identified actors in the cloud computing sphere. Five primary actors were identified - Cloud Service Consumer, Cloud Service Provider, Cloud Broker, Cloud Auditor and Cloud Carrier. Their roles and activities are discussed in this report. A primary goal for generating this model was to give the United States Government (USG) a method for understanding and communicating the components of a cloud computing system for Federal IT executives, Program Managers and IT procurement officials.
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paper_title: Special section : Federated resource management in grid and cloud computing systems

paper_content:

Welcome to the special issue of Future Generation Computer System (FGCS) journal. This special issue compiles a number of excellent technical contributions that significantly advance the state-of-the-art in federated management [2][3][4] of Grid and Cloud computing environments. Federated management of administratively distributed grids and clouds offers significant benefits including: (i) improving the ability of resource providers to meet SLA compliance [5] for clients and offer improved service by optimizing the service placement and throughput according to users' QoS needs; (ii) enhancing the peak-load handling and dynamic system expansion capacity of every Grid/Cloud domain through federation [1] without the need of setting up a new software or hardware infrastructure in every location, and (iii) adapting to failures including natural disasters and regular system maintenance more gracefully as providers can transparently migrate their services to others domains in the federation, thus avoiding SLA violations and resulting penalties. Hence, federated management not only ensures business continuity but also augments the reliability of the participating resource providers. Next, we briefly describe the technical contributions, which were selected for publication in this special issue. All of the selected papers underwent a rigorous peer-review process.
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paper_title: A taxonomy and survey of grid resource management systems for distributed computing

paper_content:

The resource management system is the central component of distributed network computing systems. There have been many projects focused on network computing that have designed and implemented resource management systems with a variety of architectures and services. In this paper, an abstract model and a comprehensive taxonomy for describing resource management architectures is developed. The taxonomy is used to identify approaches followed in the implementation of existing resource management systems for very large-scale network computing systems known as Grids. The taxonomy and the survey results are used to identify architectural approaches and issues that have not been fully explored in the research. Copyright © 2001 John Wiley & Sons, Ltd.
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paper_title: Resource provisioning for cloud computing

paper_content:

In resource provisioning for cloud computing, an important issue is how resources may be allocated to an application mix such that the service level agreements (SLAs) of all applications are met. A performance model with two interactive job classes is used to determine the smallest number of servers required to meet the SLAs of both classes. For each class, the SLA is specified by the relationship: Prob [response time ≤ x] ≥ y. Two server allocation strategies are considered: shared allocation (SA) and dedicated allocation (DA). For the case of FCFS scheduling, analytic results for response time distribution are used to develop a heuristic algorithm that determines an allocation strategy (SA or DA) that requires the smallest number of servers. The effectiveness of this algorithm is evaluated over a range of operating conditions. The performance of SA with non-FCFS scheduling is also investigated. Among the scheduling disciplines considered, a new discipline called probability dependent priority is found to have the best performance in terms of requiring the smallest number of servers.
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paper_title: Comparison of scheduling schemes for on-demand IaaS requests

paper_content:

Highlights? We investigate the interaction aspects between on-demand requests and the allocation of virtual machines in a server farm operated by a specific infrastructure owner. ? We compare scheduling algorithms from the aspect of the average energy consumption and heat emission of servers as well as the blocking probabilities of on-demand requests. ? A saving on the energy consumption is possible in the operational range (where on-demand requests do not face unpleasant blocking) with the allocation of virtual machines to physical servers based on the priority. Infrastructure-as-a-service (IaaS) is one of emerging powerful cloud computing services provided by IT industry at present. This paper considers the interaction aspects between on-demand requests and the allocation of virtual machines in a server farm operated by a specific infrastructure owner. We formulate an analytic performance model of the server farm taking into account the quality of service (QoS) guaranteed to users and the operational energy consumption in the server farm. We compare several scheduling algorithms from the aspect of the average energy consumption and heat emission of servers as well as the blocking probabilities of on-demand requests. Based on numerical results of a comparison of different allocation strategies, a saving on the energy consumption is possible in the operational range (where on-demand requests do not face unpleasant blocking probability) with the allocation of virtual machines to physical servers based on the priority.
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paper_title: VM3: Measuring, modeling and managing VM shared resources

paper_content:

With cloud and utility computing models gaining significant momentum, data centers are increasingly employing virtualization and consolidation as a means to support a large number of disparate applications running simultaneously on a chip-multiprocessor (CMP) server. In such environments, contention for shared platform resources (CPU cores, shared cache space, shared memory bandwidth, etc.) can have a significant effect on each virtual machine's performance. In this paper, we investigate the shared resource contention problem for virtual machines by: (a) measuring the effects of shared platform resources on virtual machine performance, (b) proposing a model for estimating shared resource contention effects, and (c) proposing a transition from a virtual machine (VM) to a virtual platform architecture (VPA) that enables transparent shared resource management through architectural mechanisms for monitoring and enforcement. Our measurement and modeling experiments are based on a consolidation benchmark (vConsolidate) running on a state-of-the-art CMP server. Our virtual platform architecture experiments are based on detailed simulations of consolidation scenarios. Through detailed measurements and simulations, we show that shared resource contention affects virtual machine performance significantly and emphasize that virtual platform architectures is a must for future virtualized datacenters.
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paper_title: An adaptive model-free resource and power management approach for multi-tier cloud environments

paper_content:

With the development of cloud environments serving as a unified infrastructure, the resource management and energy consumption issues become more important in the operations of such systems. In this paper, we investigate adaptive model-free approaches for resource allocation and energy management under time-varying workloads and heterogeneous multi-tier applications. Specifically, we make use of measurable metrics, including throughput, rejection amount, queuing state, and so on, to design resource adjustment schemes and to make control decisions adaptively. The ultimate objective is to guarantee the summarized revenue of the resource provider while saving energy and operational costs. To validate the effectiveness, performance evaluation experiments are performed in a simulated environment, with realistic workloads considered. Results show that with the combination of long-term adaptation and short-term adaptation, the fluctuation of unpredictable workloads can be captured, and thus the total revenue can be preserved while balancing the power consumption as needed. Furthermore, the proposed approach can achieve better effect and efficiency than the model-based approaches in dealing with real-world workloads.
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paper_title: Dynamic resource allocation and power management in virtualized data centers

paper_content:

We investigate optimal resource allocation and power management in virtualized data centers with time-varying workloads and heterogeneous applications. Prior work in this area uses prediction based approaches for resource provisioning. In this work, we take an alternate approach that makes use of the queueing information available in the system to make online control decisions. Specifically, we use the recently developed technique of Lyapunov Optimization to design an online admission control, routing, and resource allocation algorithm for a virtualized data center. This algorithm maximizes a joint utility of the average application throughput and energy costs of the data center. Our approach is adaptive to unpredictable changes in the workload and does not require estimation and prediction of its statistics.
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paper_title: On-demand provisioning of Cloud and Grid based infrastructure services for collaborative projects and groups

paper_content:

Effective use of existing network and IT infrastructure can be achieved by providing combined network and IT resources on-demand as infrastructure services that are capable of supporting complex technological processes, scientific experiments, and collaborative groups of researchers and applications. This paper provides a short overview of existing standards and technologies and refers to ongoing projects. We also describe experiences in developing an architectural framework and tools for combined on-demand network and Grid/Cloud service provisioning. The paper proposes an architectural framework for on-demand infrastructure service provisioning comprising of three main components: the Composable Services Architecture (CSA) that intends to provide a conceptual and methodological framework for developing dynamically configurable virtualised infrastructure services; the Infrastructure Services Modeling Framework (ISMF) that provides a basis for the infrastructure resources virtualisation and management, including description, discovery, modeling, composition and monitoring; and the Service Delivery Framework (SDF) that provides a basis for defining the whole composable services life cycle management and supporting infrastructure services. We discuss implementation suggestions for the defined architectural components and provides information about the ongoing developments of the GEMBus which is considered as a middleware framework for CSA.
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paper_title: Next Generation Cloud Computing Architecture: Enabling Real-Time Dynamism for Shared Distributed Physical Infrastructure

paper_content:

Cloud computing is fundamentally altering the expectations for how and when computing, storage and networking resources should be allocated, managed and consumed. End-users are increasingly sensitive to the latency of services they consume. Service Developers want the Service Providers to ensure or provide the capability to dynamically allocate and manage resources in response to changing demand patterns in real-time. Ultimately, Service Providers are under pressure to architect their infrastructure to enable real-time end-to-end visibility and dynamic resource management with fine grained control to reduce total cost of ownership while also improving agility. The current approaches to enabling real-time, dynamic infrastructure are inadequate, expensive and not scalable to support consumer mass-market requirements. Over time, the server-centric infrastructure management systems have evolved to become a complex tangle of layered systems designed to automate systems administration functions that are knowledge and labor intensive. This expensive and non-real time paradigm is ill suited for a world where customers are demanding communication, collaboration and commerce at the speed of light. Thanks to hardware assisted virtualization, and the resulting decoupling of infrastructure and application management, it is now possible to provide dynamic visibility and control of services management to meet the rapidly growing demand for cloud-based services. What is needed is a rethinking of the underlying operating system and management infrastructure to accommodate the ongoing transformation of the data center from the traditional server-centric architecture model to a cloud or network-centric model. This paper proposes and describes a reference model for a network-centric datacenter infrastructure management stack that borrows and applies key concepts that have enabled dynamism, scalability, reliability and security in the telecom industry, to the computing industry. Finally, the paper will describe a proof-of-concept system that was implemented to demonstrate how dynamic resource management can be implemented to enable real-time service assurance for network centric datacenter architecture.
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paper_title: Combining Grid and Cloud Resources for Hybrid Scientific Computing Executions

paper_content:

The advent of Cloud computing has paved the way to envision hybrid computational infrastructures based on powerful Grid resources combined with dynamic and elastic on-demand virtual infrastructures on top of Cloud deployments. However, the combination of Grid and Cloud resources for executing computationally intensive scientific applications introduces new challenges and opportunities in areas such as resource provisioning and management, meta-scheduling and elasticity. This paper describes different approaches to integrate the usage of Grid and Cloud-based resources for the execution of High Throughput Computing scientific applications. A reference architecture is proposed and the the opportunities and challenges of such hybrid computational scenarios are addressed. Finally, a prototype implementation is described and a case study that involves a protein design application is employed to outsource job executions to the Cloud when Grid resources become exhausted.
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paper_title: A tenant-based resource allocation model for scaling Software-as-a-Service applications over cloud computing infrastructures

paper_content:

Cloud computing provides on-demand access to computational resources which together with pay-per-use business models, enable application providers seamlessly scaling their services. Cloud computing infrastructures allow creating a variable number of virtual machine instances depending on the application demands. An attractive capability for Software-as-a-Service (SaaS) providers is having the potential to scale up or down application resources to only consume and pay for the resources that are really required at some point in time; if done correctly, it will be less expensive than running on regular hardware by traditional hosting. However, even when large-scale applications are deployed over pay-per-use cloud high-performance infrastructures, cost-effective scalability is not achieved because idle processes and resources (CPU, memory) are unused but charged to application providers. Over and under provisioning of cloud resources are still unsolved issues. Even if peak loads can be successfully predicted, without an effective elasticity model, costly resources are wasted during nonpeak times (underutilization) or revenues from potential customers are lost after experiencing poor service (saturation). This work attempts to establish formal measurements for under and over provisioning of virtualized resources in cloud infrastructures, specifically for SaaS platform deployments and proposes a resource allocation model to deploy SaaS applications over cloud computing platforms by taking into account their multi-tenancy, thus creating a cost-effective scalable environment. Highlights? Overutilization occurs when resource utilization gets above the point of exhaustion. ? Underutilization is the amount of resources that can be allocated within other VMs. ? Tenant-based isolation is achieved through authentication and persistence layers. ? Tenant-based allocation determines the minimum number of VM instances for workload. ? Load balancer distributes requests based on tenant context and VM availability.
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paper_title: Cloud computing and its interest in saving energy: the use case of a private cloud

paper_content:

Cloud computing data centres, due to their housing of powerful ICT equipment, are high energy consumers and therefore accountable for large quantities of emissions. Therefore, energy saving strategies applicable to such data centres are a very promising research direction both from the economical and environmental stand point. In this paper, we study the case of private cloud computing environments from the perspective of energy saving incentives. However, the proposed approach can also be applied to any computing style: cloud (both public and private), traditional and supercomputing. To this end, we provide a generic conceptual description for ICT resources of a data centre and identify their corresponding energy-related attributes. Furthermore, we give power consumption prediction models for servers, storage devices and network equipment. We show that by applying appropriate energy optimisation policies guided through accurate power consumption prediction models, it is possible to save about 20% of energy consumption when typical single-site private cloud data centres are considered. Minimising the data centre’s energy consumption, on one hand acknowledges the potential of ICT for saving energy across many segments of the economy, on the other hand helps ICT sector to show the way for the rest of the economy by reducing its own carbon footprint. In this paper, we show that it is possible to save energy by studying the case of a single-site private cloud data centres. We believe that through the federation of several cloud data centres (both private and public), it is possible to minimise both the energy consumption as well as CO2 emissions.
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paper_title: Resource provisioning for cloud computing

paper_content:

In resource provisioning for cloud computing, an important issue is how resources may be allocated to an application mix such that the service level agreements (SLAs) of all applications are met. A performance model with two interactive job classes is used to determine the smallest number of servers required to meet the SLAs of both classes. For each class, the SLA is specified by the relationship: Prob [response time ≤ x] ≥ y. Two server allocation strategies are considered: shared allocation (SA) and dedicated allocation (DA). For the case of FCFS scheduling, analytic results for response time distribution are used to develop a heuristic algorithm that determines an allocation strategy (SA or DA) that requires the smallest number of servers. The effectiveness of this algorithm is evaluated over a range of operating conditions. The performance of SA with non-FCFS scheduling is also investigated. Among the scheduling disciplines considered, a new discipline called probability dependent priority is found to have the best performance in terms of requiring the smallest number of servers.
---
paper_title: Comparison of scheduling schemes for on-demand IaaS requests

paper_content:

Highlights? We investigate the interaction aspects between on-demand requests and the allocation of virtual machines in a server farm operated by a specific infrastructure owner. ? We compare scheduling algorithms from the aspect of the average energy consumption and heat emission of servers as well as the blocking probabilities of on-demand requests. ? A saving on the energy consumption is possible in the operational range (where on-demand requests do not face unpleasant blocking) with the allocation of virtual machines to physical servers based on the priority. Infrastructure-as-a-service (IaaS) is one of emerging powerful cloud computing services provided by IT industry at present. This paper considers the interaction aspects between on-demand requests and the allocation of virtual machines in a server farm operated by a specific infrastructure owner. We formulate an analytic performance model of the server farm taking into account the quality of service (QoS) guaranteed to users and the operational energy consumption in the server farm. We compare several scheduling algorithms from the aspect of the average energy consumption and heat emission of servers as well as the blocking probabilities of on-demand requests. Based on numerical results of a comparison of different allocation strategies, a saving on the energy consumption is possible in the operational range (where on-demand requests do not face unpleasant blocking probability) with the allocation of virtual machines to physical servers based on the priority.
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paper_title: Dynamic voltage and frequency scaling: The laws of diminishing returns

paper_content:

Dynamic voltage and frequency scaling (DVFS) is a commonly-used power-management technique where the clock frequency of a processor is decreased to allow a corresponding reduction in the supply voltage. This reduces power consumption, which can lead to significant reduction in the energy required for a computation, particularly for memory-bound workloads. ::: ::: However, recent developments in processor and memory technology have resulted in the saturation of processor clock frequencies, larger static power consumption, smaller dynamic power range and better idle/sleep modes. Each of these developments limit the potential energy savings resulting from DVFS. We analyse this trend by examining the potential of DVFS across three platforms with recent generations of AMD processors. We find that while DVFS is effective on the older platforms, it actually increases energy usage on the most recent platform, even for highly memory-bound workloads.
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paper_title: Dynamic resource allocation and power management in virtualized data centers

paper_content:

We investigate optimal resource allocation and power management in virtualized data centers with time-varying workloads and heterogeneous applications. Prior work in this area uses prediction based approaches for resource provisioning. In this work, we take an alternate approach that makes use of the queueing information available in the system to make online control decisions. Specifically, we use the recently developed technique of Lyapunov Optimization to design an online admission control, routing, and resource allocation algorithm for a virtualized data center. This algorithm maximizes a joint utility of the average application throughput and energy costs of the data center. Our approach is adaptive to unpredictable changes in the workload and does not require estimation and prediction of its statistics.
---
paper_title: On-demand provisioning of Cloud and Grid based infrastructure services for collaborative projects and groups

paper_content:

Effective use of existing network and IT infrastructure can be achieved by providing combined network and IT resources on-demand as infrastructure services that are capable of supporting complex technological processes, scientific experiments, and collaborative groups of researchers and applications. This paper provides a short overview of existing standards and technologies and refers to ongoing projects. We also describe experiences in developing an architectural framework and tools for combined on-demand network and Grid/Cloud service provisioning. The paper proposes an architectural framework for on-demand infrastructure service provisioning comprising of three main components: the Composable Services Architecture (CSA) that intends to provide a conceptual and methodological framework for developing dynamically configurable virtualised infrastructure services; the Infrastructure Services Modeling Framework (ISMF) that provides a basis for the infrastructure resources virtualisation and management, including description, discovery, modeling, composition and monitoring; and the Service Delivery Framework (SDF) that provides a basis for defining the whole composable services life cycle management and supporting infrastructure services. We discuss implementation suggestions for the defined architectural components and provides information about the ongoing developments of the GEMBus which is considered as a middleware framework for CSA.
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paper_title: Evaluation of gang scheduling performance and cost in a cloud computing system

paper_content:

Cloud Computing refers to the notion of outsourcing on-site available services, computational facilities, or data storage to an off-site, location-transparent centralized facility or "Cloud." Gang Scheduling is an efficient job scheduling algorithm for time sharing, already applied in parallel and distributed systems. This paper studies the performance of a distributed Cloud Computing model, based on the Amazon Elastic Compute Cloud (EC2) architecture that implements a Gang Scheduling scheme. Our model utilizes the concept of Virtual Machines (or VMs) which act as the computational units of the system. Initially, the system includes no VMs, but depending on the computational needs of the jobs being serviced new VMs can be leased and later released dynamically. A simulation of the aforementioned model is used to study, analyze, and evaluate both the performance and the overall cost of two major gang scheduling algorithms. Results reveal that Gang Scheduling can be effectively applied in a Cloud Computing environment both performance-wise and cost-wise.
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paper_title: Failure-aware resource provisioning for hybrid Cloud infrastructure

paper_content:

Hybrid Cloud computing is receiving increasing attention in recent days. In order to realize the full potential of the hybrid Cloud platform, an architectural framework for efficiently coupling public and private Clouds is necessary. As resource failures due to the increasing functionality and complexity of hybrid Cloud computing are inevitable, a failure-aware resource provisioning algorithm that is capable of attending to the end-users quality of service (QoS) requirements is paramount. In this paper, we propose a scalable hybrid Cloud infrastructure as well as resource provisioning policies to assure QoS targets of the users. The proposed policies take into account the workload model and the failure correlations to redirect users' requests to the appropriate Cloud providers. Using real failure traces and a workload model, we evaluate the proposed resource provisioning policies to demonstrate their performance, cost as well as performance-cost efficiency. Simulation results reveal that in a realistic working condition while adopting user estimates for the requests in the provisioning policies, we are able to improve the users' QoS about 32% in terms of deadline violation rate and 57% in terms of slowdown with a limited cost on a public Cloud.
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paper_title: Adaptive resource configuration for Cloud infrastructure management

paper_content:

To guarantee the vision of Cloud Computing QoS goals between the Cloud provider and the customer have to be dynamically met. This so-called Service Level Agreement (SLA) enactment should involve little human-based interaction in order to guarantee the scalability and efficient resource utilization of the system. To achieve this we start from Autonomic Computing, examine the autonomic control loop and adapt it to govern Cloud Computing infrastructures. We first hierarchically structure all possible adaptation actions into so-called escalation levels. We then focus on one of these levels by analyzing monitored data from virtual machines and making decisions on their resource configuration with the help of knowledge management (KM). The monitored data stems both from synthetically generated workload categorized in different workload volatility classes and from a real-world scenario: scientific workflow applications in bioinformatics. As KM techniques, we investigate two methods, Case-Based Reasoning and a rule-based approach. We design and implement both of them and evaluate them with the help of a simulation engine. Simulation reveals the feasibility of the CBR approach and major improvements by the rule-based approach considering SLA violations, resource utilization, the number of necessary reconfigurations and time performance for both, synthetically generated and real-world data. Highlights? We apply knowledge management to guarantee SLAs and low resource wastage in Clouds. ? Escalation levels provide a hierarchical model to structure possible reconfiguration actions. ? Case-Based Reasoning and rule-based approach prove feasibility as KM techniques. ? In-depth evaluation of rule-based approach shows major improvements towards CBR. ? KM is applied to real-world data gathered from scientific bioinformatic workflows.
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paper_title: Combining Grid and Cloud Resources for Hybrid Scientific Computing Executions

paper_content:

The advent of Cloud computing has paved the way to envision hybrid computational infrastructures based on powerful Grid resources combined with dynamic and elastic on-demand virtual infrastructures on top of Cloud deployments. However, the combination of Grid and Cloud resources for executing computationally intensive scientific applications introduces new challenges and opportunities in areas such as resource provisioning and management, meta-scheduling and elasticity. This paper describes different approaches to integrate the usage of Grid and Cloud-based resources for the execution of High Throughput Computing scientific applications. A reference architecture is proposed and the the opportunities and challenges of such hybrid computational scenarios are addressed. Finally, a prototype implementation is described and a case study that involves a protein design application is employed to outsource job executions to the Cloud when Grid resources become exhausted.
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paper_title: Resource Allocation Using Virtual Clusters

paper_content:

We propose a novel approach for sharing cluster resources among competing jobs. The key advantage of our approach over current solutions is that it increases cluster utilization while optimizing a user-centric metric that captures both notions of performance and fairness. We motivate and formalize the corresponding resource allocation problem, determine its complexity, and propose several algorithms to solve it in the case of a static workload that consists of sequential jobs. Via extensive simulation experiments we identify an algorithm that runs quickly, that is always on par with or better than its competitors, and that produces resource allocations that are close to optimal. We find that the extension of our approach to parallel jobs leads to similarly good results. Finally, we explain how to extend our work to dynamicworkloads.
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paper_title: A tenant-based resource allocation model for scaling Software-as-a-Service applications over cloud computing infrastructures

paper_content:

Cloud computing provides on-demand access to computational resources which together with pay-per-use business models, enable application providers seamlessly scaling their services. Cloud computing infrastructures allow creating a variable number of virtual machine instances depending on the application demands. An attractive capability for Software-as-a-Service (SaaS) providers is having the potential to scale up or down application resources to only consume and pay for the resources that are really required at some point in time; if done correctly, it will be less expensive than running on regular hardware by traditional hosting. However, even when large-scale applications are deployed over pay-per-use cloud high-performance infrastructures, cost-effective scalability is not achieved because idle processes and resources (CPU, memory) are unused but charged to application providers. Over and under provisioning of cloud resources are still unsolved issues. Even if peak loads can be successfully predicted, without an effective elasticity model, costly resources are wasted during nonpeak times (underutilization) or revenues from potential customers are lost after experiencing poor service (saturation). This work attempts to establish formal measurements for under and over provisioning of virtualized resources in cloud infrastructures, specifically for SaaS platform deployments and proposes a resource allocation model to deploy SaaS applications over cloud computing platforms by taking into account their multi-tenancy, thus creating a cost-effective scalable environment. Highlights? Overutilization occurs when resource utilization gets above the point of exhaustion. ? Underutilization is the amount of resources that can be allocated within other VMs. ? Tenant-based isolation is achieved through authentication and persistence layers. ? Tenant-based allocation determines the minimum number of VM instances for workload. ? Load balancer distributes requests based on tenant context and VM availability.
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paper_title: Online optimization for scheduling preemptable tasks on IaaS cloud systems

paper_content:

In Infrastructure-as-a-Service (IaaS) cloud computing, computational resources are provided to remote users in the form of leases. For a cloud user, he/she can request multiple cloud services simultaneously. In this case, parallel processing in the cloud system can improve the performance. When applying parallel processing in cloud computing, it is necessary to implement a mechanism to allocate resource and schedule the execution order of tasks. Furthermore, a resource optimization mechanism with preemptable task execution can increase the utilization of clouds. In this paper, we propose two online dynamic resource allocation algorithms for the IaaS cloud system with preemptable tasks. Our algorithms adjust the resource allocation dynamically based on the updated information of the actual task executions. And the experimental results show that our algorithms can significantly improve the performance in the situation where resource contention is fierce.
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paper_title: Energy Conscious Scheduling for Distributed Computing Systems under Different Operating Conditions

paper_content:

Traditionally, the primary performance goal of computer systems has focused on reducing the execution time of applications while increasing throughput. This performance goal has been mostly achieved by the development of high-density computer systems. As witnessed recently, these systems provide very powerful processing capability and capacity. They often consist of tens or hundreds of thousands of processors and other resource-hungry devices. The energy consumption of these systems has become a major concern. In this paper, we address the problem of scheduling precedence-constrained parallel applications on multiprocessor computer systems and present two energy-conscious scheduling algorithms using dynamic voltage scaling (DVS). A number of recent commodity processors are capable of DVS, which enables processors to operate at different voltage supply levels at the expense of sacrificing clock frequencies. In the context of scheduling, this multiple voltage facility implies that there is a trade-off between the quality of schedules and energy consumption. To effectively balance these two performance goals, we have devised a novel objective function and a variant from that. The main difference between the two algorithms is in their measurement of energy consumption. The extensive comparative evaluations conducted as part of this work show that the performance of our algorithms is very compelling in terms of both application completion time and energy consumption.
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paper_title: Low level Metrics to High level SLAs - LoM2HiS framework: Bridging the gap between monitored metrics and SLA parameters in cloud environments

paper_content:

Cloud computing represents a novel on-demand computing approach where resources are provided in compliance to a set of predefined non-functional properties specified and negotiated by means of Service Level Agreements (SLAs). In order to avoid costly SLA violations and to timely react to failures and environmental changes, advanced SLA enactment strategies are necessary, which include appropriate resource-monitoring concepts. Currently, Cloud providers tend to adopt existing monitoring tools, as for example those from Grid environments. However, those tools are usually restricted to locality and homogeneity of monitored objects, are not scalable, and do not support mapping of low-level resource metrics e.g., system up and down time to high-level application specific SLA parameters e.g., system availability. In this paper we present a novel framework for managing the mappings of the Low-level resource Metrics to High-level SLAs (LoM2HiS framework). The LoM2HiS framework is embedded into FoSII infrastructure, which facilitates autonomic SLA management and enforcement. Thus, the LoM2HiS framework detects future SLA violation threats and can notify the enactor component to act so as to avert the threats. We discuss the conceptual model of the LoM2HiS framework, followed by the implementation details. Finally, we present the first experimental results and a proof of concept of the LoM2HiS framework.
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paper_title: VM3: Measuring, modeling and managing VM shared resources

paper_content:

With cloud and utility computing models gaining significant momentum, data centers are increasingly employing virtualization and consolidation as a means to support a large number of disparate applications running simultaneously on a chip-multiprocessor (CMP) server. In such environments, contention for shared platform resources (CPU cores, shared cache space, shared memory bandwidth, etc.) can have a significant effect on each virtual machine's performance. In this paper, we investigate the shared resource contention problem for virtual machines by: (a) measuring the effects of shared platform resources on virtual machine performance, (b) proposing a model for estimating shared resource contention effects, and (c) proposing a transition from a virtual machine (VM) to a virtual platform architecture (VPA) that enables transparent shared resource management through architectural mechanisms for monitoring and enforcement. Our measurement and modeling experiments are based on a consolidation benchmark (vConsolidate) running on a state-of-the-art CMP server. Our virtual platform architecture experiments are based on detailed simulations of consolidation scenarios. Through detailed measurements and simulations, we show that shared resource contention affects virtual machine performance significantly and emphasize that virtual platform architectures is a must for future virtualized datacenters.
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paper_title: An architecture model of management and monitoring on Cloud services resources

paper_content:

As a new computing paradigm, Cloud computing has been advocated by IT industry and academia. It can provide lots of services, however, how to manage these services and guarantee the quality of service, has become one of the key factors for the development of Cloud computing. This paper proposes a hierarchical architecture model of Cloud services resources management and monitoring by integrating the power of Service-Oriented Architecture and the concept of IT service management. The architecture model uses service monitoring and process of IT service management to realize management of Cloud services resources, and then provides foundation for technology to ensure the quality of service of Cloud services. Therefore, it could be used as a reference architecture model of management and monitoring on Cloud services resources.
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paper_title: A Runtime Model Based Monitoring Approach for Cloud

paper_content:

Monitoring plays a significant role in improving the quality of service in cloud computing. It helps clouds to scale resource utilization adaptively, to identify defects in services for service developers, and to discover usage patterns of numerous end users. However, due to the heterogeneity of components in clouds and the complexity arising from the wealth of runtime information, monitoring in clouds faces many new challenges. In this paper, we propose a runtime model for cloud monitoring (RMCM), which denotes an intuitive representation of a running cloud by focusing on common monitoring concerns. Raw monitoring data gathered by multiple monitoring techniques are organized by RMCM to present a more intuitive profile of a running cloud. We applied RMCM in the implementation of a flexible monitoring framework, which can achieve a balance between runtime overhead and monitoring capability via adaptive management of monitoring facilities. Our experience of utilizing the monitoring framework on a real cloud demonstrates the feasibility and effectiveness of our approach.
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paper_title: Research of grid resource monitoring based on event-trigger and fixed polling

paper_content:

Grid is commonly considered as next generation network, under the heterogeneous high performance distributed computing environment, it is significant to monitor the state information of the resources obtained by each computing node. Resource monitoring plays a critical role in managing a large-scale Grid system. Traditional resources monitoring needs to read the information of each node periodically, the grid resources are consumed relatively greatly. Resource monitoring based on event-trigger can reduce the expenditure on service, but can not provide more exactter resource state information. This paper presents a method based on event-trigger and fixed polling for reducing the expenditure on monitor service and obtaining exact resource state information. Finally, a grid resource monitor system is implemented by this method in GlobusToolkit4 and an objective evaluation of the system is presented.
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Title: A Survey on Resource Allocation and Monitoring in Cloud Computing

Section 1: INTRODUCTION
Description 1: Introduce the topic of cloud computing, its service models, and the importance of resource allocation and monitoring.

Section 2: OVERVIEW OF CLOUD COMPUTING
Description 2: Provide definitions, characteristics, deployment models, and service models of cloud computing.

Section 3: RESOURCE MANAGEMENT AT A GLANCE
Description 3: Outline the processes of resource management including discovery, allocation, and monitoring.

Section 4: RESOURCE PROVISIONING AND MONITORING PROCESS
Description 4: Detail the provisioning and monitoring processes undertaken by service and infrastructure providers.

Section 5: RELATED WORK IN RESOURCE MANAGEMENT
Description 5: Discuss the challenges and problems in resource management, and review existing solutions and mechanisms in resource allocation and monitoring.

Section 6: RESOURCE ALLOCATION
Description 6: Review various scheduling algorithms and strategies used for optimizing resource allocation.

Section 7: RESOURCE MONITORING
Description 7: Analyze different mechanisms and techniques implemented for resource monitoring in cloud computing.

Section 8: CONCLUSION
Description 8: Summarize the findings on resource allocation and monitoring strategies, highlighting their impact on performance, efficiency, and SLA compliance.