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A Heuristic Placement Selection of Live Virtual Machine Migration for Energy-Saving in Cloud Computing Environment

Author

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  • Jia Zhao
  • Liang Hu
  • Yan Ding
  • Gaochao Xu
  • Ming Hu

Abstract

The field of live VM (virtual machine) migration has been a hotspot problem in green cloud computing. Live VM migration problem is divided into two research aspects: live VM migration mechanism and live VM migration policy. In the meanwhile, with the development of energy-aware computing, we have focused on the VM placement selection of live migration, namely live VM migration policy for energy saving. In this paper, a novel heuristic approach PS-ES is presented. Its main idea includes two parts. One is that it combines the PSO (particle swarm optimization) idea with the SA (simulated annealing) idea to achieve an improved PSO-based approach with the better global search's ability. The other one is that it uses the Probability Theory and Mathematical Statistics and once again utilizes the SA idea to deal with the data obtained from the improved PSO-based process to get the final solution. And thus the whole approach achieves a long-term optimization for energy saving as it has considered not only the optimization of the current problem scenario but also that of the future problem. The experimental results demonstrate that PS-ES evidently reduces the total incremental energy consumption and better protects the performance of VM running and migrating compared with randomly migrating and optimally migrating. As a result, the proposed PS-ES approach has capabilities to make the result of live VM migration events more high-effective and valuable.

Suggested Citation

  • Jia Zhao & Liang Hu & Yan Ding & Gaochao Xu & Ming Hu, 2014. "A Heuristic Placement Selection of Live Virtual Machine Migration for Energy-Saving in Cloud Computing Environment," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-13, September.
    • Handle: RePEc:plo:pone00:0108275
    DOI: 10.1371/journal.pone.0108275
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    References listed on IDEAS

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    1. R. Jeyarani & N. Nagaveni & R. Vasanth Ram, 2011. "Self Adaptive Particle Swarm Optimization for Efficient Virtual Machine Provisioning in Cloud," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 7(2), pages 25-44, April.
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    Cited by:

    1. Hancui Zhang & Shuyu Chen & Jun Liu & Zhen Zhou & Tianshu Wu, 2017. "An incremental anomaly detection model for virtual machines," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-23, November.

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