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

Shuxiang Li,Xianbing Pan

doi:10.1186/s13638-020-01722-4

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.

Highlights

Data centers usually use virtualization technology to provide all kinds of cloud storage or computing services for users
2.1 Objective optimization function of adaptive management Aiming at the multi-objective optimization of virtual machine placement technology, the particle swarm optimization (PSO) method is used to construct an adaptive management target optimization model of virtual machines in cloud computing [20, 21]
The objective optimization model of adaptive management of virtual machine placement in cloud computing is constructed by particle swarm evolution method [22, 23]

Summary

Introduction

Data centers usually use virtualization technology to provide all kinds of cloud storage or computing services for users. One of the three indexes of application performance and power consumption can only minimize the temperature or increase the utilization rate of resources or reduce power consumption. It cannot be effective at the same time [3, 4]. To study host selection algorithm, it is necessary to consider temperature, resource usage, application performance, and power consumption, and optimize virtual machine placement, so that the data center can obtain lower temperature while effectively guaranteeing the application performance, higher resource utilization, and less power consumption [7, 8]. Simulation experiments show this method in adaptive management to realize virtual machine placement in cloud computing and multiobjective optimization of superior performance

Methods

Multi-objective optimization problem description

Results and discussion

Funding None