Virtual Machine Deployment Strategy Based on Improved PSO in Cloud Computing

Authors

  • Shanchen Pang College of Computer Science and Technology, China University of Petroleum, 266580 Qingdao City, Shandong, China
  • Dekun Dong College of Computer Science and Technology, China University of Petroleum, 266580 Qingdao City, Shandong, China
  • Shuyu Wang College of Computer Science and Technology, China University of Petroleum, 266580 Qingdao City, Shandong, China

DOI:

https://doi.org/10.31577/cai_2020_1-2_83

Keywords:

Cloud computing, Pareto optimal solution, particle swarm optimization algorithm, resource reservation, virtual machine deployment

Abstract

Energy consumption is an important cost driven by growth of computing power, thereby energy conservation has become one of the major problems faced by cloud system. How to maximize the utilization of physical machines, reduce the number of virtual machine migrations, and maintain load balance under the constraints of physical machine resource thresholds that is the effective way to implement energy saving in data center. In the paper, we propose a multi-objective physical model for virtual machine deployment. Then the improved multi-objective particle swarm optimization (TPSO) is applied to virtual machine deployment. Compared to other algorithms, the algorithm has better ergodicity into the initial stage, improves the optimization precision and optimization efficiency of the particle swarm. The experimental results based on CloudSim simulation platform show that the algorithm is effective at improving physical machine resource utilization, reducing resource waste, and improving system load balance.

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Published

2020-03-24

How to Cite

Pang, S., Dong, D., & Wang, S. (2020). Virtual Machine Deployment Strategy Based on Improved PSO in Cloud Computing. Computing and Informatics, 39(1-2), 83–104. https://doi.org/10.31577/cai_2020_1-2_83

Issue

Section

Special Section Articles