摘要
针对传统的遗传算法在云环境中迭代次数多、耗时长的缺陷,提出了一种改进的遗传算法,主要从执行时间及执行任务所需的费用两个方面来优化任务调度.通过建立任务调度模型,设计出相应的适应度函数、界限函数.仿真结果表明,在任务调度中运用改进的遗传算法,所需的平均等待时间要短,调度所需的费用也比传统的遗传算法要低.
An improved genetic algorithm was proposed to solve the problem of more iterative times and cost more time in traditional genetic algorithm. The algorithm was designed to optimize the task scheduling mainly from two aspects： the execution time and the cost of the mission. The corresponding fitness function and boundary function were designed by establishing the task scheduling model. The results showed that,in task scheduling using genetic algorithm,the average waiting time was shorter and scheduling costs required lower than that of the traditional genetic algorithm.
出处
《信阳师范学院学报（自然科学版）》
CAS
北大核心
2015年第3期438-441,共4页
Journal of Xinyang Normal University(Natural Science Edition)
基金
海南省应用技术研发与示范推广专项项目(zdxm2014087)
海南省教育科学"十二五"规划课题(QJY125119)
三亚市院地科技合作项目(2014YD14
2014YD24
2014YD30
2014YD39)
三亚市农业科技创新项目(2014NK26)
三亚市重点实验室建设项目(L1410)
信阳师范学院青年科研基金(2012058)
关键词
云计算
任务调度
遗传算法
cloud computing
task scheduling
genetic algorithm
