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Optimal resource provisioning for cloud computing environment

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Abstract

The paper presents an efficient cloud resource provisioning approach. The Software as a Service (SaaS) provider leases resources from cloud providers and also leases software as services to SaaS users. The SaaS providers aim at minimizing the payment of using VMs from cloud providers, and want to maximize the profit earned through serving the SaaS users’ requests. The SaaS providers also guarantee meeting quality of service (QoS) requirements of the SaaS users. The cloud provider is to maximize the profit without exceeding the upper bound of energy consumption of cloud provider for provisioning virtual machines (VMs) to the SaaS provider. The SaaS users purpose to obtain the optimized QoS to accomplish their jobs with a limited budget and deadline. The proposed optimal cloud resource provisioning algorithm includes two sub-algorithms at different levels: interaction between the SaaS user and SaaS provider at the application layer and interaction between the SaaS provider and cloud resource provider at the resource layer. Simulations are conducted to compare the performance of proposed cloud resource provisioning algorithm with related work.

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Acknowledgements

The work was partly supported by the National Natural Science Foundation of China (NSF) under Grant Nos. 60970064, 61171075; the National Key Basic Research Program of China (973 Program) under Grant No. 2011CB302601; the Open Fund of the State Key Laboratory of Software Development Environment under Grant No. SKLSDE-2011KF-01; the Beihang University, Fok Ying Tong Education Foundation, China under Grant No. 121067. Any opinions, findings, and conclusions are those of the authors and do not necessarily reflect the views of the above agencies.

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  1. Department of Computer Science, Wuhan University of Technology, Wuhan, 430063, P.R. China

    Chunlin Li & La Yuan Li

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  1. Chunlin Li
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  2. La Yuan Li
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Correspondence to Chunlin Li.

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Li, C., Li, L.Y. Optimal resource provisioning for cloud computing environment. J Supercomput 62, 989–1022 (2012). https://doi.org/10.1007/s11227-012-0775-9

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