计算机技术与发展2016，Vol.26Issue(7)：6-10,5.DOI:10.3969/j.issn.1673-629X.2016.07.002
优化粒子群的云计算任务调度算法
Task Scheduling Algorithm of Cloud Computing Based on Particle Swarm Optimization
摘要
Abstract
How to schedule cloud tasks efficiently is one of the important issues to be resolved in cloud computing. The Extremum Dis-turbed Correlative Particle Swarm Optimization ( EDCPSO) algorithm based on basic Particle Swarm Optimization ( PSO) is proposed for the characteristics of cloud environment. It uses the Copular function to measure correlation structures among random factors,support of particles properly using the individual experience and social sharing information,resolving the demerit that the PSO algorithm lacks of the global optimization ability because of not considering the function of the random factors in the optimization process. Moreover,it uses the strategy of adding extremum disturbed arithmetic operators to improve further the PSO,which resolves the demerit of falling into local extremum in the late evolution for PSO. Simulation shows that EDCPSO is better than PSO and Cloudsim original scheduling algorithm in the same experiment conditions. That is to say,the algorithm can reduce the total completion time of tasks.关键词
任务调度/云计算/粒子群优化/相关性/极值扰动Key words
task scheduling/cloud computing/PSO/correlation/disturbed extremum分类
信息技术与安全科学引用本文复制引用
谭文安,查安民,陈森博..优化粒子群的云计算任务调度算法[J].计算机技术与发展,2016,26(7):6-10,5.基金项目
国家自然科学基金资助项目(6127036) （6127036）
上海第二工业大学重点学科(XXKZD1301) （XXKZD1301）
