Soft Computing

, Volume 19, Issue 8, pp 2265–2273

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

  • Bo Xu
  • Zhiping Peng
  • Fangxiong Xiao
  • Antonio Marcel Gates
  • Jian-Ping Yu
Methodologies and Application

DOI: 10.1007/s00500-014-1406-6

Cite this article as:
Xu, B., Peng, Z., Xiao, F. et al. Soft Comput (2015) 19: 2265. doi:10.1007/s00500-014-1406-6

Abstract

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.

Keywords

Virtual machine deployment Particle swarm optimization Multi-objective optimization Cloud computing 

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Bo Xu
    • 1
    • 2
  • Zhiping Peng
    • 1
  • Fangxiong Xiao
    • 2
    • 3
  • Antonio Marcel Gates
    • 4
  • Jian-Ping Yu
    • 5
    • 6
  1. 1.Guangdong Provincial Key Lab of Petrochemical Equipment Fault Diagnosis, Department of Computer Science and TechnologyGuangdong University of Petrochemical TechnologyGuangdong China
  2. 2.School of Software EngineeringSouth China University of TechnologyGuangdong China
  3. 3.School of Information and StatisticsGuangxi University of Finance and EconomicsGuangxi China
  4. 4.Hawaii Pacific UniversityHonoluluUSA
  5. 5.Key Laboratory of High Performance Computing and Stochastic Information Processing (Ministry of Education of China), College of Mathematics and Computer ScienceHunan Normal UniversityHunan China
  6. 6.High Technology Research Key Laboratory of Wireless Sensor Networks of Jiangsu ProvinceNanjing University of Posts and TelecommunicationsJangshu China

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