Int J Performability Eng ›› 2021, Vol. 17 ›› Issue (1): 135-142.doi: 10.23940/ijpe.21.01.p13.135142

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IPSOMC: An Improved Particle Swarm Optimization and Membrane Computing based Algorithm for Cloud Computing

Kun Li*, Liwei Jia, and Xiaoming Shi   

  1. Henan Medical College, Zhengzhou, Henan, 451191, China
  • Submitted on ; Revised on ; Accepted on
  • Contact: * Corresponding author. E-mail address: sunlik_1982@126.com
  • About author:
    Kun Li is currently a Lecturer at Henan Medical College. His research interests include cloud computing and algorithm design.
    Liwei Jia is currently a Lecturer at Henan Medical College. His research interests include cloud computing and algorithm design.
    Xiaoming Shi is currently a Lecturer at Henan Medical College. His research interests include cloud computing and algorithm design.

Abstract:

In order to improve the efficiency of task scheduling under Cloud computing, this paper proposes an optimized Improved Particle Swarm Optimization and Membrane Computing (IPSOMC). First, it describes the cloud computing task scheduling model with time and cost as the main research object. Second, it uses Kent mapping to initialize the population and introduce domain particles to improve the global optimization ability of the particle swarm. It also uses the weight factor and nonlinear extreme value disturbance to improve the local optimization ability of the particle swarm. Finally, the optimal solution of the particle swarm algorithm is selected with the help of the evolution rules of membrane computing. The simulation experimental results show that the IPSOMC algorithm and the comparison algorithm have good effects in terms of completion time and consumption cost under different task scales and improve the efficiency of task scheduling.

Key words: cloud computing, particle swarm optimization, membrane computing