Resource Allocation Using Virtual Clusters

Stillwell, Mark; Schanzenbach, David; Vivien, Frédéric; Casanova, Henri

doi:10.1109/ccgrid.2009.23

Cited by 52 publications

(38 citation statements)

References 33 publications

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“…For the better solution of coordinate the resource using between the multiple users which has their own virtual machine independently, this paper presents a novel resource allocation strategy in which the slowdown degree [14] of user's job is regard as a new metric of QoS and plays the key role in the whole process of resources allocation. This new strategy allows the system to dynamically adjust the amount of resource belonging to the specific user.…”

Section: Introductionmentioning

confidence: 99%

A Novel Resource Allocation Strategy of Virtual Cluster Based on QOS

Liu¹

2012

Proceedings of the 2nd International Conference on Computer Application and System Modeling

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Abstract-How to effectively allocate computer resource to satisfy the users' different QoS requirement is one of most important issues of building virtual high performance computing environment. To address this problem, this paper presents a novel resource allocate strategy in which the slowdown degree of users' job plays the key role of resource allocation. To implement the strategy, this paper also presents a Mixed Bin Packing Algorithms which is the combination of traditional Bin-Packing Algorithm and some general Algorithms. Experimental results show that the Mixed Bin Packing Algorithm is more effective than the previous algorithms, especially in a large scale application background.

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Section: Introductionmentioning

confidence: 99%

A Novel Resource Allocation Strategy of Virtual Cluster Based on QOS

Liu¹

2012

Proceedings of the 2nd International Conference on Computer Application and System Modeling

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“…In previous work we proposed an efficient algorithm for off-line minimum yield maximization [32]. Although interesting from a theoretical point of view, this off-line algorithm cannot be applied in practice to our on-line problem: job requests arrive non-deterministically and a resource allocation decision must be made upon the arrival of each request.…”

Section: A System Overview and Use Of Vm Technologymentioning

confidence: 99%

“…We also consider algorithms that try to optimize the resource allocation globally. To this end, we build on the MCB8 algorithm [32], which is itself based on an algorithm proposed by Leinberger et. al.…”

Section: B Non-greedy Algorithmsmentioning

confidence: 99%

Dynamic fractional resource scheduling for HPC workloads

Stillwell

Vivien

Casanova

2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing (IPDPS)

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Abstract-We propose a novel job scheduling approach for homogeneous cluster computing platforms. Its key feature is the use of virtual machine technology for sharing resources in a precise and controlled manner. We justify our approach and propose several job scheduling algorithms. We present results obtained in simulations for synthetic and real-world High Performance Computing (HPC) workloads, in which we compare our proposed algorithms with standard batch scheduling algorithms. We find that our approach widely outperforms batch scheduling. We also identify a few promising algorithms that perform well across most experimental scenarios. Our results demonstrate that virtualization technology coupled with lightweight scheduling strategies affords dramatic improvements in performance for HPC workloads.

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“…One of the earliest researches used scheduling algorithm, where the first come first serve [8] method is applied, by determining the smallest number of servers required to meet the SLAs for shared allocation and dedicated allocation resources. Consequently, many of the researches embark on scheduling algorithm such as in [24] proposed an energyconscious scheduling algorithm, while in paper [20], [25], [26] use gang scheduling resource allocation, In paper [25] implemented multi-tiered resource scheduling allocation scheme using virtual clusters to develop a more efficient algorithm. In addition, it is also to determine the preferred allocation strategy of resources.…”

Section: A Resource Allocationmentioning

confidence: 99%

“…However, the works only run in one scenario and not for different scenarios with variety of workload especially for High Performance Application (HPC). M. Stillwell et al [25] conducted a study on resource allocation using virtual clusters which run multiple competing tasks. They have established optimal algorithms which compute allocations of task for different number of nodes.…”

Section: A Resource Allocationmentioning

confidence: 99%

A Survey on Resource Allocation and Monitoring in Cloud Computing

Mohamaddiah¹,

Abdullah²,

Subramaniam³

et al. 2014

IJMLC

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Abstract-The cloud provider plays a major role especially providing resources such as computing power for the cloud subscriber to deploy their applications on multiple platforms anywhere; anytime. Hence the cloud users still having problem for resource management in receiving the guaranteed computing resources on time. This will impact the service time and the service level agreements for various users in multiple applications. Therefore there is a need for a new resolution to resolve this problem. This survey paper conducts a study in resource allocation and monitoring in the cloud computing environment. We describe cloud computing and its properties, research issues in resource management mainly in resource allocation and monitoring and finally solutions approach for resource allocation and monitoring. It is believed that this paper would benefit both cloud users and researchers for further knowledge on resource management in cloud computing.Index Terms-Resource allocation, resource monitoring, cloud computing, resource management. I. INTRODUCTIONCloud Computing, a service model was introduced to provide computing resources such as computing power, storage and bandwidth to deliver the IT services to the organization. This service model is rapidly being adopted by many organizations because it offers lots of business opportunity especially in terms of financial investment and human capital. With cloud services, the organization may opt out to set up a data center for its IT infrastructure or to procure hardware and software for its business applications as they can lease the resources from the cloud service provider. Initial providers in cloud computing such as Amazon EC2 [1], Google Apps Engine [2] , Microsoft Azure [3], SalesForce.com [4] offers great business value to the interested organizations who want to subscribe the services with a concept pay-per-use, on-demand and a defined Service level agreements (SLAs). For example, Google Apps Engine offers monthly uptime percentage: 99.00% -< 99.95% for client for its covered services. For the downtime period, they offer a period of five consecutive minutes of downtime. This value is an attractive package for the client, because they manage to control the usage of resources and demands more, faster and reliable infrastructure. Manuscript received October 11, 2013; revised December 7, 2013 On the other hand, the resources on the cloud are pooled in order to serve multiple subscribers. The provider use multi-tenancy model where the resources (physical and virtual) are reassigned dynamically based on the tenant requirement [5]. The assigning of the resources will be based on the lease and SLA agreement, whereby different clients will need more or less amount of virtual resources. Subsequently, the growth of demands for cloud services is bringing more challenge for the provider to provide the resources to the client subscriber. Therefore, in this paper we provide a review on cloud computing which focus on resource management: allocation and monitoring. Our methodolo...

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Resource Allocation Using Virtual Clusters

Cited by 52 publications

References 33 publications

A Novel Resource Allocation Strategy of Virtual Cluster Based on QOS

A Novel Resource Allocation Strategy of Virtual Cluster Based on QOS

Dynamic fractional resource scheduling for HPC workloads

A Survey on Resource Allocation and Monitoring in Cloud Computing

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