基于改进粒子群优化算法的虚拟机迁移选择策略研究

Research of Live Migration of Virtual Machines Selection Strategy Optimization Problems Based on Modified Particle Swarm Optimization

粒子群优化算法由于实现容易、精度高、收敛快,在解决多目标优化问题时呈现出较强的优越性。在定义匹配距离的基础上,引入粒子群优化算法思想制定虚拟机迁移选择策略,并对粒子群优化算法做出改进,引入规避列表思想,将剩余性能不满足虚拟机性能需求的服务器加入到规避列表中,以避免多个满足非劣最优解的虚拟机迁移到一台服务器,导致资源占用率超过结点资源上限。通过在CloudSim平台上与基本粒子群优化算法进行的仿真对比实验证明了本算法具有更快的收敛速度和选择速度。

Due to easy implementation,high accuracy,fast convergence,particle swarm optimization algorithm is considered to have advantages in solving the problem of multi-objective optimization.Virtual machine migration selection policies are defined based on the definition of matching distance and the theory of particle swarm optimization algorithm.Moreover,the particle swarm optimization algorithm is improved by introducing the ideas of avoid list.In this way,the servers,which do not have enough remaining performance to meet the demand of the virtual machine,are added to the avoid list.Therefore,it can avoid multiple virtual machines that satisfy pareto optimal solutions to migrate to the same server,causing the resource usage rate to exceed the maximum resources limit of the node.Simulation experiment was done based on the CloudSim platform,and compared with basic particle swarm optimization algorithm,our algorithm was proved to have faster speed of convergence and choice.