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Energy-efficiency virtual machine placement based on binary gravitational search algorithm

  • Foudil Abdessamia
  • Wei-Zhe ZhangEmail author
  • Yu-Chu Tian
Article
  • 27 Downloads

Abstract

Cloud computing is a remarkable growing paradigm for hosting and offering services through the Internet. It attracted the most notorious business companies and resulted to an exponential increase of its users from simple end users to companies that deploy more and more of their system over the cloud. The amount of resources to provide the demand became tremendous. therefore, a great need energy supply. The world as we know is highly concerned about the environment and the energy-efficiency in all aspect of life and the domain of IT is one them. To deal with energy wastage in data centers, researches use Virtual machine placement as a main key to assure cloud consolidation and reduce power wastage. Several approaches were proposed for Virtual machine placement. This paper proposes a solution based on Binary gravitational search algorithm (BGSA) for the virtual machine placement in the heterogeneous data center. In this work, we compared the BGSA method to fit with virtual machines in data centers with particle swarm optimization, First-fit, Best-fit, and worst-fit. results showed significant difference of energy save comparing to other strategies. The results obtained gave the advantage to our approach and its better response with the increase of number of virtual machines.

Keywords

Cloud computing Green computing Energy-efficiency Optimization 

Notes

Acknowledgements

This work was supported in part by the National Key Research and Development Plan under Grant 2017YFB0801801, in part by the National Science Foundation of China (NSFC) under Grant 61672186 and Grant 61872110. Professor Zhang is the corresponding author.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.Peng Cheng LaboratoryShenzhenChina
  3. 3.School of Electrical Engineering and Computer ScienceQUTBrisbaneAustralia

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