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

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

Journal of computer and system sciences, 2013-12, Vol.79 (8), p.1230-1242 [Periódico revisado por pares]

Elsevier Inc

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  • Título:
    A multi-objective ant colony system algorithm for virtual machine placement in cloud computing
  • Autor: Gao, Yongqiang ; Guan, Haibing ; Qi, Zhengwei ; Hou, Yang ; Liu, Liang
  • Assuntos: Algorithms ; Ant colony optimization ; Cloud computing ; Multi-objective optimization ; Virtual machine placement
  • É parte de: Journal of computer and system sciences, 2013-12, Vol.79 (8), p.1230-1242
  • Notas: ObjectType-Article-2
    SourceType-Scholarly Journals-1
    ObjectType-Feature-1
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  • Descrição: 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. •This study is the first application of ACS to multi-objective VM placement problem.•We improve the efficiency of algorithm by speeding up the calculation of heuristics.•We improve the effectiveness of algorithm by increasing the learning of ants.•Our algorithm is suitable for large size of data centers with thousands of VMs.•Experiments show our algorithm outperforms other state-of-the-art algorithms.
  • Editor: Elsevier Inc
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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