摘要
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.
作者
田祥宏
徐胜超
Tian Xianghong;Xu Shengchao(School of Computer Engineering,Jinling Institute of Technology,Nanjing 211169,Jiangsu,China;School of Date Science,Guangzhou Huashang College,Guangzhou 511300,Guangdong,China)
出处
《计算机应用与软件》
北大核心
2022年第2期100-107,207,共9页
Computer Applications and Software
基金
广州华商学院校内导师制科研项目(2020HSDS04,2021HSDS15)
广西高校中青年教师基础能力提升项目(2018KY0651)。
关键词
粒子群优化算法
能量消耗模型
虚拟机迁移
虚拟机放置
智能计算
Particle swarm optimization
Energy consumption model
Virtual machine migration
Virtual machine placement
Intelligent computing