SAVE: self-adaptive consolidation of virtual machines for energy efficiency of CPU-intensive applications in the cloud | The Journal of Supercomputing
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SAVE: self-adaptive consolidation of virtual machines for energy efficiency of CPU-intensive applications in the cloud

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Abstract

In virtualized data centers, consolidation of virtual machines (VMs) on minimizing the number of total physical machines (PMs) has been recognized as a very efficient approach. This paper considers the energy-efficient consolidation of VMs in a cloud datacenter. Concentrating on CPU-intensive applications, the objective is to schedule all requests non-preemptively, subjecting to constraints of PM capacities and running time interval spans, to make the total energy consumption of all PMs is minimized (called MinTE for abbreviation). The MinTE problem is NP-complete in general. We propose a self-adaptive approach called SAVE. The approach makes decisions of the assignment and migration of VMs by probabilistic processes and is based exclusively on local information. Both simulation and real environment test show that our proposed method SAVE can reduce energy consumption about \(30\%\) against VMWare DRS and 10–20% against ecoCloud on average. Extensive experiments show that our method outperforms the existing method and achieves significant energy savings and high utilization.

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Acknowledgements

This research is sponsored by the Natural Science Foundation of China (NSFC) Grants 61672136, 61828202; and Xi Bu Zhi Guang Plan of Chinese Academy of Science (R51A150Z10), and Science and Technology Plan of Sichuan Province (2016GZ0322).

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Authors and Affiliations

  1. University of Electronic Science and Technology of China (UESTC), Chengdu, China

    Wenxia Guo, Ping Kuang, Yaqiu Jiang, Xiang Xu & Wenhong Tian

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  1. Wenxia Guo
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  2. Ping Kuang
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  3. Yaqiu Jiang
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  5. Wenhong Tian
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Correspondence to Wenxia Guo.

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Guo, W., Kuang, P., Jiang, Y. et al. SAVE: self-adaptive consolidation of virtual machines for energy efficiency of CPU-intensive applications in the cloud. J Supercomput 75, 7076–7100 (2019). https://doi.org/10.1007/s11227-019-02927-1

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