摘要： 现有云计算任务调度算法为追求最短完成时间不能很好地兼顾成本。为此，提出一种基于改进粒子群的任务调度算法。采用间接编码方式对每个子任务占用的资源进行编码，给出解码方式，定义考虑时间和成本的适应度函数，确立粒子位置和速度的更新方法。实验结果表明，在相同的条件设置下，该算法的总任务完成时间和总任务完成成本小于传统粒子群优化算法。

关键词: 云计算, 任务调度, 时间成本, 双适应度粒子群优化, 粒子群优化算法

Abstract: Existing task schedule algorithms for cloud computing are not well to take into account the cost of all the tasks for the pursuit of the shortest completion time. To solve this problem, a task schedule algorithm based on improved particle swarm is proposed in this paper, which uses indirect encoding to encode the resources of each subtask takes, gives decoding way, considers the fitness function about the time and the cost, and establishes the particle position and velocity updating method. Experimental results show that the general assignment finished time and cost of this algorithm are lower than the traditional Particle Swarm Optimization(PSO) algorithm.

Key words: cloud computing, task schedule, time cost, Double-fitness Particle Swarm Optimization(DFPSO), Particle Swarm Optimization(PSO) algorithm

中图分类号: 

• TP393