基于分布估计蛙跳算法的云资源调度方法

摘要

Focusing on the problem of high efficiency resource scheduling in cloud computing environment,since current re-source scheduling algorithm had been less given consideration to the shortest completion time and the least cost of the services, this paper designed the fitness function which could comprehensive response the time and cost,and proposed an estimation of distribution-shuffled frog leaping algorithm (EDSFLA).This algorithm redefined the evolutional operators of shuffled frog lea-ping algorithm (SFLA),utilizing the cross operators of genetic algorithm,targeted in applying to scheduling problem with in-teger-coded.EDSFLA introduced evolutionary strategy of estimation of distribution to break the confine of search pattern in the standard SFLA,and made the learning ability of this algorithm more comprehensive.Experimental results show that this algo-rithm has more capability in convergence and searching ability compared with the standard SFLA and EDA in solving cloud re-source scheduling problems.%针对云计算环境中的资源调度很少同时兼顾最短完成时间和最低服务成本的问题，设计能够综合反映时间和成本的适应度函数，在此基础上提出了基于分布估计蛙跳算法的云资源调度方法。结合遗传算法的交叉操作重新定义蛙跳算法的进化算子，使其适用于整数编码的调度问题；引入分布估计进化策略，突破了标准蛙跳算法搜索模式的局限，使算法具有更全面的学习能力。仿真实验结果表明，在云资源调度问题的求解中，该算法的收敛性能和寻优能力均优于标准的蛙跳算法和分布估计算法。