An energy efficient ant colony system for virtual machine placement in cloud computing

Xiao Fang Liu, Zhi Hui Zhan, Jeremiah D. Deng, Yun Li, Tianlong Gu, Jun Zhang

Research output: Contribution to journalArticle

117 Citations (Scopus)
367 Downloads (Pure)

Abstract

Virtual machine placement (VMP) and energy efficiency are significant topics in cloud computing research. In this paper, evolutionary computing is applied to VMP to minimize the number of active physical servers, so as to schedule underutilized servers to save energy. Inspired by the promising performance of the ant colony system (ACS) algorithm for combinatorial problems, an ACS-based approach is developed to achieve the VMP goal. Coupled with order exchange and migration (OEM) local search techniques, the resultant algorithm is termed an OEMACS. It effectively minimizes the number of active servers used for the assignment of virtual machines (VMs) from a global optimization perspective through a novel strategy for pheromone deposition which guides the artificial ants toward promising solutions that group candidate VMs together. The OEMACS is applied to a variety of VMP problems with differing VM sizes in cloud environments of homogenous and heterogeneous servers. The results show that the OEMACS generally outperforms conventional heuristic and other evolutionary-based approaches, especially on VMP with bottleneck resource characteristics, and offers significant savings of energy and more efficient use of different resources.

Original languageEnglish
Pages (from-to)113-128
Number of pages16
JournalIEEE Transactions on Evolutionary Computation
Volume22
Issue number1
Early online date21 Nov 2016
DOIs
Publication statusPublished - 26 Jan 2018

Keywords

  • ant colony system (ACS)
  • cloud computing
  • virtual machine placement (VMP)

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