Virtual machine resource allocation optimization in cloud environments with customized particle swarm optimization

Abstract

Efficient virtual machine resource allocation is vital in cloud infrastructures to maximize performance and minimize resource overprovisioning. State-of-the-art methods include virtual machine migration, placement, scheduling, and load balancing. This research investigates a customized Particle Swarm Optimization (PSO) algorithm to optimize the performance and resource utilization of virtual machines in the cloud. A cloud simulation environment was developed in Java and Python for conducting experiments using PSO. The results are promising since virtual machines’ resource allocation and performance significantly improved in only a few iterations demonstrating that the proposed PSO is an effective method for optimizing the performance and resource allocation in a cloud environment.