Abstract: The number of user is huge in Cloud Computing, and the number of tasks and the amount of data are also huge. How to schedule tasks efficiently is an important issue to be resolved in Cloud Computing environment. An Double-Fitness Genetic Algorithm (DFGA) is brought up for the programming framework of Cloud Computing. Through this algorithm the better task scheduling result which has not only shorter total-task-completion time and also has shorter average-completion time can be found out. There is a contrast between DFGA and Adaptive Genetic Algorithm (AGA) through simulation experiment, and the result is: the DFGA is better, it is an efficiently task scheduling algorithm in Cloud Computing environment.

Key words: cloud computing, Genetic Algorithm (GA), Double-Fitness Genetic Algorithm, task scheduling

摘要： 在云计算中面对的用户群是庞大的，要处理的任务量与数据量也是十分巨大的。如何对任务进行高效的调度成为云计算中所要解决的重要问题。针对云计算的编程模型框架，提出了一种具有双适应度的遗传算法（DFGA），通过此算法不但能找到总任务完成时间较短的调度结果，而且此调度结果的任务平均完成时间也较短。通过仿真实验将此算法与自适应遗传算法（AGA）进行比较，实验结果表明，此算法优于自适应遗传算法，是一种云计算环境下有效的任务调度算法。

关键词: 云计算, 遗传算法, 双适应度遗传算法, 任务调度