基于粒子群优化与蚁群优化的云计算任务调度算法

摘要

在云计算环境中用户数量众多,系统要处理的任务量十分巨大,为了使系统能够高效地完成服务请求,如何对任务进行调度成为云计算研究的重点.提出一种基于粒子群优化和蚁群优化的任务调度算法,该算法首先利用粒子群优化算法迅速求得初始解,然后根据该调度结果生成蚁群算法的初始信息素分布,最后利用蚁群算法得到任务调度的最优解.通过在CloudSim平台进行仿真实验,表明该算法具有较好的实时性和寻优能力,是一种有效的调度算法.%In cloud computing environment, there are a large number of users which lead to huge amount of tasks to be processed by system. In order to make the system complete the service requests efficiently, how to schedule the tasks becomes the focus of cloud computing research. A task scheduling algorithm based on PSO and ACO for cloud computing is presented in this paper. First, the algorithm uses particle swarm optimisation algorithm to get the initial solution quickly, and then according to this scheduling result the initial pheromone distribution of ant colony algorithm is generated. Finally, the ant colony algorithm is used to get the optimal solution of task scheduling. The experiment simulated on CloudSim platform shows that the algorithm has good effect in real-time performance and optimisation capability. It is an effective task scheduling algorithm.