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。
The number of users 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. A Double-Fitness Genetic Algorithm （DFGA） was brought up for the programming framework of cloud computing. Through this algorithm, the better task scheduling not only shortens total-task-completion time and also has shorter average-completion time. 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 efficient task scheduling algorithm in cloud computing environment.
出处
《计算机应用》
CSCD
北大核心
2011年第1期184-186,共3页
journal of Computer Applications
基金
四川省科技支撑计划项目(06KJT-013
2009GZ0153)
关键词
云计算
遗传算法
双适应度
任务调度
cloud computing
Genetic Algorithm （GA）
double-fitness
task scheduling
