Virtual Machine Scheduling in Cloud Environment Based on Annealing Algorithm and Improved Particle Swarm Algorithm

Abstract

In order to reduce the power consumption in the cloud environment and the placement of virtual machines in the cloud environment, this paper proposes an annealing algorithm to optimize the placement of the particle swarm by analyzing the particle swarm algorithm. This algorithm optimizes the particle swarm algorithm in all directions, and dynamically optimizes the inertia coefficient of the particle swarm algorithm based on the Gaussian function. With the help of simulated annealing algorithm, the local optimal position is disturbed to improve the ability of jumping out of the local optimal. Optimize the total energy consumption of the data center as the objective function. Based on the relationship between the local optimal solution, the global optimal solution, and the inertia coefficient, the particle swarm algorithm is improved. The simulation experiments of CloudSim, a cloud computing simulation platform, show that the improved algorithm has better convergence speed, higher optimization accuracy, and reduced power consumption.