中文摘要：

针对云计算中的任务调度问题,提出了一种任务调度的增强蚁群算法（task scheduling-enhanced ant colony optimization,TS-EACO）。算法兼顾了任务调度的最短完成时间和负载平衡,同时参考了近年来蚁群算法的各种改进,创新地将任务在虚拟机上的一次分配作为蚂蚁的搜索对象。实验在CloudSim仿真平台下进行,并将仿真结果与Round Robin算法和标准蚁群算法进行比较,结果表明TS-EACO算法的任务执行时间和负载平衡性能均优于这两种算法。

英文摘要：

To deal with problems for task schedule of cloud computing, a design method of task scheduling enhanced ant colony optimization （task scheduling-enhanced ant colony optimization, TS-EACO） algorithm is proposed. A balance of the minimum execution time and load balance of task schedule are gotten of this algorithm. The TS-EACO also absorbs the advantages of some refine ant colony algorithms occurred recent years. An allocation of a task for a virtual machine is an object that the ant would search. Some experiments are done on the CloudSim platform. The results of three different algorithms are compared. The comparison shows the execution time and load balance of TS-EACO are better than those of others.