云环境下计算资源调度策略与仿真研究

Cloud Computing Resource Scheduling Strategy Based on Catfish Particle Swarm Optimization Algorithm

为提高云计算资源利用率,提出一种基于鲶鱼粒子群优化算法的云计算资源调度策略(CF-PSO)。首先根据云计算资源的属性对资源进行分类,将任务和资源编码粒子,任务完成时间最小化作为目标函数,然后通过粒子之间的信息交流和协作,找到最优资源调度策略,并引入"鲶鱼"效应,对传统粒子群优化算法进行改进,保持粒子的多样性,最后在CloudSim平台上进行仿真。仿真结果表明,相对于传统粒子群优化算法,CF-PSO算法不仅提高了云计算资源利用率,而且缩短了任务完成时间。

In order to improve the utilization rate of the cloud computing resource, this paper proposed a cloud computing resource scheduling strategy based on catfish particle swarm optimization algorithm （CF-PSO）. Firstly, the resources were classified based on the cloud computing resource attributes, the task and resource were encoded as a particle, the task completion time minimization was taken as the objective function, and then the optimal resource scheduling strategy was found by information exchanges and cooperation among particles, and ＂catfish effect＂ was in- troduced into particle swarm optimization algorithm to maintain the diversity of particles. Finally, the simulation ex- periment was carried out on the cloudSim platform. The simulation results show that, compared with traditional parti- cle swarm optimization algorithm, the proposed algorithm not only improves the utilization rate of the cloud computing resource, but also shortens the complete time of the task.