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Adaptive management and multi-objective optimization of virtual machine in cloud computing based on particle swarm optimization

机译:基于粒子群优化的云计算虚拟机的自适应管理和多目标优化

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摘要

Abstract In order to improve the adaptive management ability of virtual machine placement in cloud computing, an adaptive management and multi-objective optimization method for virtual machine placement in cloud computing is proposed based on particle swarm optimization (PSO). The objective optimization model of adaptive management of virtual machine placement in cloud computing is constructed by particle swarm evolution, and the global optimization control of adaptive management of virtual machine placement in cloud computing is carried out by introducing extremum perturbation operator. The global dynamic objective function of particle swarm optimization is constructed, and the global optimal solution of virtual machine in cloud computing is found by deconvolution algorithm, and the optimal position of particle swarm is searched in two-dimensional space. The multi-objective optimization problem of adaptive management of virtual machine placement is transformed into particle swarm optimization problem to realize adaptive management and multi-objective optimization of virtual machine placement in cloud computing. Simulation results show that the adaptive management of virtual machine placement in cloud computing using this method has better global optimization ability, better convergence of particle swarm optimization, and better performance of multi-objective optimization.
机译:摘要为了提高适应性管理虚拟机放置在云计算用于在云计算虚拟机放置能力,适应性管理和多目标优化的方法是基于粒子群优化(PSO)算法。在云计算虚拟机放置的适应性管理的目标优化模型由粒子群进化构造,并且在云计算虚拟机放置的适应性管理的全局优化控制是通过引入极值扰动算子进行。的粒子群优化全局动态目标函数构造,并且在云计算虚拟机的全局最优解是通过解卷积算法找到,并且粒子群的最佳位置中搜索二维空间。虚拟机放置的适应性管理的多目标优化问题转化为粒子群优化问题以实现适应性管理和在云计算虚拟机放置的多目标优化。仿真结果表明,虚拟机放置在云中的适应性管理使用这种方法计算具有更好的全局优化能力,粒子群算法更好的收敛性和多目标优化的更好的性能。

著录项

  • 作者

    Shuxiang Li; Xianbing Pan;

  • 作者单位
  • 年度 2020
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
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