模糊云资源调度问题的RIOPSO算法

RIOPSO Algorithm for Fuzzy Cloud Resource Scheduling Problem

针对时间-成本约束下的云资源调度问题,使用三角模糊数表示不确定的任务执行时间,建立了模糊云资源调度模型,调度的目标是降低任务总的执行时间和总的成本消耗,决策变量是任务和虚拟机的映射关系。使用混合粒子群优化算法(RIOPSO)对模糊云资源调度进行求解。该算法使用了正交初始化粒子群的方法,提升粒子初始探索最优调度方案的质量,在粒子搜索过程中使用重新随机化控制粒子的搜索范围,使用实时更新惯性权重的方式控制粒子在搜索中的速度,从而得到最优的调度方案。在Cloudsim仿真平台上使用随机生成的仿真数据,对提出的问题模型和优化算法进行验证,证明了模型的可靠性,实验结果表明使用提出的优化算法,可以达到使云资源调度中总执行时间和总执行成本降低的目的,并且在收敛速度、求解能力方面具有良好的性能。

To solve the cloud resource scheduling problem under time-cost constraints,a triangular fuzzy number is used to represent the uncertain task execution time,and a fuzzy cloud resource scheduling model is established.The objective function of scheduling model is to reduce the total execution time and total cost consumption of the task,and the decision variables are the mapping relationship between tasks and virtual machines.The re-randomization inertia weight orthogonal initialization particle swarm optimization algorithm(RIOPSO)is proposed to solve the fuzzy cloud resource scheduling.This algorithm uses the method of orthogonal initialization particle swarm optimiza-tion to improve the quality of the initial exploration of the optimal scheduling scheme.In the process of particle search,re-randomization is used to control the search range of particles,and real-time updating of inertia weight is used to control the speed of particles,and to obtain the optimal scheduling scheme.The randomly generated simula-tion data on the Cloudsim simulation platform are used to verify the problem model and optimization algorithm proposed in this paper,which proves the reliability of the model.The experimental results show that RIOPSO algorithm can reduce the total execution time and cost in cloud resource scheduling,and it has good performance in convergence speed and solving ability.