Journal of South China University of Technology (Natural Science Edition) ›› 2015, Vol. 43 ›› Issue (9): 95-99.doi: 10.3969/j.issn.1000-565X.2015.09.015

• Computer Science & Technology • Previous Articles     Next Articles

Improved Discrete Particle Swarm-Based Parallel Schedule Algorithm in Cloud Computing Environment#br#

Xu Hua  Zhang Ting   

  1. School of Internet of Things Engineering，Jiangnan University，Wuxi 214122，Jiangsu，China
  • Received:2014-09-28 Revised:2015-03-28 Online:2015-09-25 Published:2015-09-07
  • Contact: 徐华(1978-)，女，博士，副教授，主要从事人工神经网络、模糊系统、水污染等研究． E-mail:joanxh2003@163.com
  • About author:徐华(1978-)，女，博士，副教授，主要从事人工神经网络、模糊系统、水污染等研究．
  • Supported by:
    Supported by the National Scholarship Fund Program(201308320030) and the Natural Science Foundation of
    Jiangsu Province(BK20140165)

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Abstract: Aiming at the optimization problem of task scheduling in the cloud computing environment and the defects of prematurity and low precision of traditional discrete particle swarm optimization (DPSO) algorithms，a
method of dynamically adjusting the inertia weight factor is proposed in a cloud computing environment，and an improved discrete particle swarm optimization algorithm is put forward． This algorithm can determine the appropriate parallel task allocation scheme quickly，and makes the scheme achieve the shortest scheduling length． Simulation results show that the improved DPSO algorithm is superior to the traditional DPSO algorithm and the genetic algorithm in terms of the convergence，the previous global search capability and the late local exploration performance，and that，in the case of a large number of tasks，the parallel task scheduling algorithm using the improved DPSO algorithm is superior to those using the traditional DPSO algorithm or the genetic algorithm in terms of scheduling length．

Key words: cloud computing, parallel algorithms, discrete particle swarm optimization