Multi-Objective Virtual Machine Placement Algorithm Based on Improved Discrete Differential Evolution

Abstract

Aiming at the problem of high energy consumption and resource fragmentation caused by unbalanced multidimensional resource usage of servers in current cloud data centers, a virtual machine placement algorithm based on improved discrete differential evolution(IDDE) algorithm was proposed. According to the multi-dimensional resource requirements of virtual machines, the population initialization was used to improve the convergence speed of the algorithm, and the discrete differential mutation and crossover operations were used to ensure the diversity of the population. A multi-group elite selection strategy based on $\varepsilon$ relaxation was proposed to select the optimal virtual machine placement scheme and enhance the global search ability of the algorithm. The simulation results show that compared with the other three algorithms such as the DE algorithm, the IDDE algorithm has a certain improvement effect in reducing energy consumption, improving resource utilization and reducing resource fragmentation.