云平台下基于粒子群优化算法的虚拟机迁移模型

A PARTICLE SWARM OPTIMIZATION FOR VIRTUAL MACHINE MIGRATION IN CLOUD PLATFORM

提出基于粒子群优化的虚拟机迁移模型(Particle swarm optimization for virtual machine migration model,PSO-VMM)。设计基于多维物理资源约束的能量消耗模型,以能量消耗最小作为粒子群优化的目标函数。在物理主机状态检测和虚拟机选择阶段,利用鲁棒局部归约检测LRR(Local Regression Robust)和最小迁移时间选择MMT(Minimum Migration Time)。在虚拟机放置阶段,将粒子群优化算法应用到大规模的候选迁移虚拟机到物理主机的重新分配。仿真实验结果表明:PSO-VMM迁移策略使得云平台的各类性能指标都得到改善。

A particle swarm optimization for virtual machine migration model(PSO-VMM)is proposed in this paper.The energy consumption model based on multidimensional physical resource constraints was designed,and the minimized energy consumption was used as the objective function.The local regression robust(LRR)algorithm and minimum migration time(MMT)policy were adopted in the physical host status detection and virtual selection procedure.In the virtual placement,PSO was applied to large-scale candidate migration and reallocation of virtual machines to physical hosts.The experimental results show that PSO-VMM migration improves various performance indicators of the cloud platform.