基于自适应惩罚函数的云工作流调度协同进化遗传算法  

Coevolutionary Genetic Algorithm of Cloud Workflow Scheduling Based on Adaptive Penalty Function

作　　者：徐健锐[1,2] 朱会娟 XU Jian-rui;ZHU Hui-juan(School of Computer Science and Telecommunication Engineering,Jiangsu University,Zhenjiang,Jiangsu 212013,China;Zhenjiang Branch,Jiangsu Union Technical Institute,Zhenjiang,Jiangsu 212016,China;School of Computer and Control Engineering,University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]江苏大学计算机科学与通信工程学院,江苏镇江212013 [2]江苏联合职业技术学院镇江分院,江苏镇江212016 [3]中国科学院大学计算机与控制学院,北京100049

出　　处：《计算机科学》2018年第8期105-112,共8页Computer Science

基　　金：国家自然科学基金项目(61302124);江苏省高校自然科学研究面上项目(16KJB520010)资助

摘　　要：云计算为大规模科学工作流应用的执行提供了更高效的运行环境。为了解决云环境中科学工作流调度的代价优化问题,提出了一种基于协同进化的工作流调度遗传算法CGAA。该算法将自适应惩罚函数引入严格约束的遗传算法中,通过协同进化的方法,自适应地调整种群个体的交叉与变异概率,以加速算法收敛并防止种群早熟。通过4种科学工作流的仿真实验结果表明,CGAA算法得到的调度方案在满足工作流调度截止时间约束与降低任务执行代价的综合性能方面优于同类型算法。The cloud computing provides a more efficient operation environment for the execution of large-scale scientific workflow application.To solve the cost optimization problem of the scientific workflow scheduling in the cloud environment,a workflow scheduling genetic algorithm based on coevolution was proposed.This algorithm introduces an adaptive penalty function into GA with the strict constraints.By the coevolutionary approach,it can adjust the crossover and mutation probability of population individuals adaptively to accelerate the convergence of the algorithm and prevent the prematurity of population.The simulation experiment results of four kinds of scientific workflow in reality show that the scheduling scheme obtained by the CGAA algorithm performs better in satisfying the comprehensive performance of the workflow scheduling deadline constraints and reducing the total execution cost of tasks compared with the same types of algorithms.

关 键 词：云计算 科学工作流 任务调度 协同进化 遗传算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]