基于蚁群模拟退火算法的云环境任务调度

Task Scheduling Algorithm Based on Simulated Annealing Ant Colony Algorithm in Cloud Computing Environment

针对云计算的MapReduce编程框架,提出一种融合蚁群算法和模拟退火算法的混合调度算法(ACOSA).该算法以最小化调度时间为目标,引入了任务与资源的匹配因子和负载均衡度,先利用蚁群算法得到一组任务到资源的优化解,然后通过模拟退火算法对解进行路径的优化和信息素的更新.通过扩展Cloudsim云计算仿真平台,对其进行重新编译,实现了所提出的算法,实验结果表明该算法在调度时间、负载均衡等方面表现良好.

It studies the task scheduling in cloud computing , and proposes a hybrid scheduling algorithm （ ACOSA） combined with ant colony algorithm and simulated annealing algorithm for the MapReduce pro-gramming framework of cloud computing .This algorithm aims at minimizing the scheduling time and in-troduces the task and resource matching factors and load balance .Firstly, the ant colony algorithm was used to get the optimal solution to a set of tasks and resources .Then, the path was optimized , and the pheromone of solution was updated by the simulated annealing algorithm .Lastly, they were recompiled by extending Cloudsim cloud computing simulation platform , and the ACOSA algorithm was achieved .The experimental results show that the algorithm has a good performance in scheduling time and load balan -cing .