基于竞争粒子群算法的云计算资源调度策略

Cloud Computing Resource Scheduling Strategy Based on Competitive Particle Swarm Algorithm

针对大规模云计算环境下的资源调度问题,提出了改进的竞争粒子群优化算法,以提高云计算资源调度效率.基于多目标综合评价模型,首先建立包含任务完成时间、功耗以及负载均衡度的适应度函数,再利用混沌优化方法产生分布更加均匀的初始化粒子,引入自适应概率的高斯变异对胜利粒子位置进行更新,以提高种群多样性并增强全局搜索能力.仿真试验表明,在相同的条件下,本文算法能够寻到最佳的调度方案,适用于大规模资源调度,且结果优于对比模型.

To solve the resource scheduling problem in the large-scale cloud computing environment,this paper proposes an improved competitive particle swarm optimization algorithm(ICSO)to improve the efficiency of resource scheduling in cloud computing.Based on the multi-objective comprehensive evaluation model,firstly,the fitness function including task completion time,power consumption and load balance are established.Then,the more evenly distributed initialization particles are generated by the chaos optimization method,and the Gaussian mutation of adaptive probability is introduced to update the position of the victory particles,so as to improve the population diversity and enhance the global search ability.Simulation results show that under the same conditions,the algorithm can find the best scheduling scheme,which is suitable for large-scale resource scheduling,and the results are better than the comparison model.