改进的蚁群算法在云计算资源调度中的应用

摘要

To improve the resource allocation efficiency of cloud computing,a time,cost and load balance-enhanced ant colony optimization (TCLB-EACO)of task execution was proposed.Information elements were improved and information was inspired creatively by making a comprehensive reference of the latest ant colony algorithms.Some experiments were carried out on the CloudSim platform,and the results were compared with algorithms of ACO and the latest LBACO.The comparison shows TCLB-EACO algorithm is more efficient than the other two algorithms in reducing time and cost of task execution and in keeping load balance,thus optimizes resource utilization in the system.%为提高云计算环境下资源调度的效率,提出一种基于时间成本负载加强型的蚁群算法 TCLB-EACO (time,cost and load balance-enhanced ant colony optimization),在综合参考各种最新蚁群算法的基础上,创新地改进信息素和启发信息。利用CloudSim工具进行仿真测试,与标准 ACO 算法、最新 LBACO 算法做仿真对比,实验结果表明,TCLB-EACO算法在任务的执行时间、成本以及系统负载均衡方面均优于这两种算法,提高了系统资源利用率。