A Virtual Machine Placement Strategy with Low Resource Consumption

Abstract

A virtual machine placement strategy based on sine and cosine perturbation and reverse learning particle swarm optimization is proposed to solve the problem of insufficient optimization of internal resource consumption in data center. First of all, the integer encoding method is used to solve the shortcoming of the tedious operation of binary encoding on the virtual machine placement problem. Secondly, the quality of the initial solution is improved by the inverse learning strategy to initialize the population, the method of sine and cosine perturbation is used to avoid the particle swarm optimization algorithm falling into the locally optimal solution, and the ability of exploration and development is explored by the open downward parabola adaptive adjustment. Then, with minimizing resource consumption as the optimization goal, a constrained optimization model for virtual machine placement in the data center is established. Finally, the relevant experiments prove that this strategy can effectively reduce resource consumption and ensure service quality, and it has a good application prospect.