Resource-Efficient VM Placement in the Cloud Environment Using Improved Particle Swarm Optimization

Abstract

Fundamentally, a strategy considering the effective utilization of resources results in the better energy efficiency of the system. The aroused interest of users in cloud computing has led to an increased power consumption making the network operation costly. The frequent requests from the users asking for computing resources can lead to instability in the load of the computing system. To perform the load balancing in the host, migration of the virtual machines from the overloaded and underloaded hosts needs to be done, which is considered an important facet concerning energy consumption. The proposed Particle Swarm Optimization based Resource Aware VM Placement (RAPSO_VMP) scheme aims to place the migrated virtual machines. RAPSO_VMP takes into consideration multiple resources like CPU, storage, and memory while trying to optimize the overall resource utilization of the system. According to the simulation analysis, the proposed RAPSO_VMP scheme shows an improvement of 5.51% in energy consumption, reduced the number of migrations by 9.12%, and the number of hosts shutdowns 22.74%.