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CEVP: Cross Entropy based Virtual Machine Placement for Energy Optimization in Clouds

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Abstract

Big data trends have recently brought unrivalled opportunities to the cloud systems. Numerous virtual machines (VMs) have been widely deployed to enable the on-demand provisioning and pay-as-you-go services for customers. Due to the large complexity of the current cloud systems, promising VM placement algorithm are highly desirable. This paper focuses on the energy efficiency and thermal stability issues of the cloud systems. A Cross Entropy based VM Placement (CEVP) algorithm is proposed to simultaneously minimize the energy cost, total thermal cost and the number of hot spots in the data center. Simulation results indicate that the proposed CEVP algorithm can (1) achieve energy savings of 26.2 % on average, (2) efficiently reduce the temperature cost by up to 6.8 % and (3) significantly decrease the total number of the hot spots by 60.1 % on average in the cloud systems, by comparing to the Ant Colony System-based algorithm.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Nos. 61501411 and 61440018) and the China Postdoctoral Science Foundation (2014M552112).

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Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan, 430074, People’s Republic of China

    Xiaodao Chen & Yunliang Chen

  2. School of Information Technologies, The University of Sydney, Sydney, Australia

    Albert Y. Zomaya

  3. Newcastle University, Newcastle, UK

    Rajiv Ranjan

  4. Department of Electrical and Computer Engineering, Houghton, MI, 49931, USA

    Shiyan Hu

Authors
  1. Xiaodao Chen
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  2. Yunliang Chen
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  3. Albert Y. Zomaya
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  4. Rajiv Ranjan
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  5. Shiyan Hu
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Corresponding author

Correspondence to Yunliang Chen.

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Chen, X., Chen, Y., Zomaya, A.Y. et al. CEVP: Cross Entropy based Virtual Machine Placement for Energy Optimization in Clouds. J Supercomput 72, 3194–3209 (2016). https://doi.org/10.1007/s11227-016-1630-1

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  • DOI: https://doi.org/10.1007/s11227-016-1630-1

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