一种基于改进二进制粒子群的工作流云调度算法  

A Workflow Cloud Scheduling Algorithm Based on Improved Binary Particle Swarm Optimization

作　　者：熊聪聪[1] 高萌[1] 赵青[1] 徐丹滢 XIONG Congcong;GAO Meng;ZHAO Qing;XU Danying(College of Artificial Intelligence,Tianjin University of Science&Technology,Tianjin 300457,China)

机构地区：[1]天津科技大学人工智能学院,天津300457

出　　处：《天津科技大学学报》2021年第4期61-66,共6页Journal of Tianjin University of Science & Technology

基　　金：国家自然科学基金青年项目(11803022);天津市科委应用基础与前沿技术研究计划项目(18JCQNJC69800)。

摘　　要：针对批处理科学工作流这一类应用,解决云环境中任务分配问题,从而有效降低成本,提高资源利用率,提出了一种改进的二进制粒子群算法.尽管传统的二进制粒子群算法具有很强的全局探测能力,但难以收敛于全局最优位置,而且随着迭代次数的不断增加,后期的搜索能力差.本文对粒子的更新公式进行修改,改善原始二进制粒子群算法的收敛性,提高了最优解的探测能力.实验结果表明,该算法所得最优解具有更好的实际调度时间和更少的资源租赁成本.An improved binary particle swarm optimization algorithm is proposed in this article to solve the problem of task allocation in cloud environment so as to effectively reduce cost and improve resource utilization.Although the traditionalbinary particle swarm optimization algorithm has strong global detection capability,it is difficult to converge to the global optimal location.Moreover,with the increase of iteration times,the search capability in the later stage is poor.In the article,the particle updating formula was modified to improve the convergence of the original binary particle swarm optimization algorithm and improve the detection ability of the optimal solution.The experimental results showed that the optimal solution obtained by the algorithm had better actual scheduling time and less resource leasing cost.

关 键 词：云计算 任务调度 二进制粒子群算法 批处理科学工作流 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]