云计算环境中基于分布估计蛙跳算法的资源调度

Resource Scheduling Method Based on Estimation of Distribution Shuffled Frog Leaping Algorithm for Cloud Computing

针对云计算环境下的资源调度问题,提出了一种基于分布估计蛙跳算法的云资源调度策略。在运用混合蛙跳算法(SFLA)搜索全局最优解的同时,在SFLA的局部搜索环节引入基于群体的增量学习算法(PBILA),通过建立反映优质解分布的概率模型,增加子群间的协作、增强群体的全面学习能力。仿真实验结果表明:该资源调度策略不仅能够有效地避免陷入局部最优,而且较好地提升了全局收敛性能。

Aiming at the problem of resource scheduling for cloud computing,a resource scheduling method based on estimation of distribution shuffled frog leaping is proposed in this paper.In applying shuffled frog leaping algorithm（SFLA） into searching the global optimal solution,this paper introduces population based incremental learning algorithm（PBILA） in the local search part of SFLA.By establishing the probability model reflecting the distribution of high quality solutions,cooperation of the subgroup is increased and the overall learning ability of the group is enhanced.Simulation results show that the resource scheduling method proposed in this paper not only can effectively avoid the local optimum,but also improved the global convergence performance.