吉林大学学报（理学版）2016，Vol.54Issue(5)：1077-1081,5.DOI:10.13413/j.cnki.jdxblxb.2016.05.26
基于遗传-蚁群算法的云计算任务调度优化
Task Scheduling and Optimization of Cloud Computing Based on Genetic Algorithm and Ant Colony Algorithm
摘要
Abstract
In order to find the best cloud computing task scheduling scheme and shorten task completion time of cloud computation,by a comprehensive consideration of advantages of genetic algorithm and ant colony algorithm, we proposed a new cloud computing task scheduling and optimization algorithm based on genetic algorithm and ant colony algorithm. Firstly, genetic algorithm was used to search feasible scheme of cloud computing task scheduling.Secondly,feasible scheme was used to initialize pheromone distribution of ant colony algorithm,to solve problem of lack initial pheromone,to speed up convergence speed and search ability,and to improve the efficiency of cloud computing tasks.Finally the experimental results on CloudSim platform show that compared with genetic algorithm.The proposed algorithm is more suitable for solving the problem of large-scale cloud computing tasks,which shortens task scheduling time,and user satisfaction is higher.关键词
云计算/遗传算法/任务调度/任务完成时间/蚁群算法Key words
cloud computing/genetic algorithm/task scheduling/task completion time/ant colony algorithm分类
信息技术与安全科学引用本文复制引用
曹阳,刘亚军,俞琰..基于遗传-蚁群算法的云计算任务调度优化[J].吉林大学学报（理学版）,2016,54(5):1077-1081,5.基金项目
江苏省高校自然科学基金(批准号:14KJB520004) （批准号:14KJB520004）
