A multi-objective ant colony system algorithm for virtual machine placement in cloud computing

  • Authors:

  • Yongqiang Gao;Haibing Guan;Zhengwei Qi;Yang Hou;Liang Liu

  • Affiliations:

  • Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;UM-SJTU Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, China;IBM Research - China, Beijing 100193, China

  • Venue:
  • Journal of Computer and System Sciences
  • Year:
  • 2013

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Abstract

Virtual machine placement is a process of mapping virtual machines to physical machines. The optimal placement is important for improving power efficiency and resource utilization in a cloud computing environment. In this paper, we propose a multi-objective ant colony system algorithm for the virtual machine placement problem. The goal is to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption. The proposed algorithm is tested with some instances from the literature. Its solution performance is compared to that of an existing multi-objective genetic algorithm and two single-objective algorithms, a well-known bin-packing algorithm and a max-min ant system (MMAS) algorithm. The results show that the proposed algorithm is more efficient and effective than the methods we compared it to.