摘要
为提高云计算任务调度的服务质量(QoS),提出一种多群智能算法的云计算任务调度策略。首先利用全局搜索能力强的遗传算法快速找到云计算任务调度问题的较优解,然后将较优解转换成蚁群优化算法的初始信息素,最后通过蚂蚁间的信息交流和反馈找到云计算任务调度的全局最优解。以CloudSim为仿真平台进行了模拟实验,结果表明,与同类算法相比,多群智能算法不仅大幅提高了云计算任务调度效率,而且减少了处理请求任务的平均完成时间。
In order to improve the QoS of task scheduling of cloud computing,this paper proposed a cloud computing task scheduling model based on warm intelligent algorithm.Firstly,genetic algorithm which has global search ability is used to quickly find he optimal solutions of cloud computing task ssheduling problem,and then the optimal solutions are converted to the initial pheromone of ant colony optimization algorithm,finally,the global optimal solution of cloud computing task scheduling is obtained by information communication and feedback among ants.The simulated experiment was carried out on cloudsim platform.The experimental results show that compared with other models,the proposed model significantly improves the efficiency of cloud computing tasks scheduling and reduces the completion time.
出处
《计算机科学》
CSCD
北大核心
2014年第S1期83-86,共4页
Computer Science
基金
国家社会科学基金项目(06BFX051)
上海高校选拔培养优秀青年教师科研专项基金(hzf05046)资助
关键词
云计算
遗传算法
蚁群优化算法
任务调度
Cloud computing,Genetic algorithm,Ant colony opt optimization algorithm,Task scheduling
