计算机工程与应用 ›› 2013, Vol. 49 ›› Issue (19): 68-72.

• 网络、通信、安全 • 上一篇    下一篇

基于改进粒子群算法的云计算任务调度算法

张  陶,于  炯,杨兴耀,廖  彬   

  1. 新疆大学 信息科学与工程学院,乌鲁木齐 830046
  • 出版日期:2013-10-01 发布日期:2015-04-20

Improved Particle Swarm Optimization algorithm for?cloud?computing?task scheduling

ZHANG Tao, YU Jiong, YANG Xingyao, LIAO Bin   

  1. School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
  • Online:2013-10-01 Published:2015-04-20

摘要: 如何对任务进行高效合理的调度是云计算需要解决的关键问题之一,针对云计算的编程模型框架,在传统粒子群优化算法(PSO)的基础上,提出了一种具有双适应度的粒子群算法(DFPSO)。通过该算法不但能找到任务总完成时间较短的调度结果,而且此调度结果的任务平均完成时间也较短。仿真分析结果表明,在相同的条件设置下,该算法优于传统的粒子群优化算法,当任务数量增多时,其综合调度性能优点明显。

关键词: 任务调度, 云计算, 粒子群算法, 双适应度

Abstract: How to schedule tasks efficiently is one of the key issues to be resolved in cloud computing environment. A Double Fitness Particle Swarm Optimization algorithm(DFPSO) based on conventional Particle Swarm Optimization(PSO) is brought up for the programming framework of cloud computing. Through this algorithm, the better task scheduling not only shortens total task completion time and also has shorter average task completion time. Simulation results show that DFPSO is better than PSO, and the integrated scheduling performance is excellent, especially when the number of tasks increases.

Key words: task scheduling, cloud computing, Particle Swarm Optimization(PSO) algorithm, double-fitness