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Dynamic deployment of virtual machines in cloud computing using multi-objective optimization

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Cloud computing is regarded as the fifth utility service and is the next generation of computation. The computing resources can be dynamically allocated according to consumer requirements and preferences Virtual machine deployment has an important role in cloud computing, and aims to reduce turnaround times and improve resource use. In essence, the deployment of virtual machines is a multi-objective decision problem that must consider key factors. That is, we need to optimize the resource use and migration times. In this paper, we propose the multi-objective comprehensive evaluation model for the dynamic deployment of virtual machines. We then use an improved multi-objective particle swarm optimization (IMOPSO) to solve the problem. We have designed two simulation experiments using the CloudSim toolkit: the first experimental results show that on comparison of our improved algorithm with the traditional single-objective algorithms PSO and QPSO, our method is feasible and efficient; the second experimental results show that IMOPSO can search effectively, maintain population diversity, and quickly converge to the Pareto optimal solution without losing stability. The obtained Pareto optimal solution set has a better convergence and distribution than a comparative method.

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This research is supported by the National Natural Science Foundation of China (Grant No. 61272382), Science and Technology Foundation for the Universities of Guangxi Province (Grant No. 2013ZD060), the Hunan Provincial Natural Science Foundation of China (Grant No. 12JJ6063), and Guangdong Province Science and Technology Project (Grant No. 2012B010100037).

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Correspondence to Zhiping Peng.

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Communicated by V. Loia.

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Xu, B., Peng, Z., Xiao, F. et al. Dynamic deployment of virtual machines in cloud computing using multi-objective optimization. Soft Comput 19, 2265–2273 (2015).

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