Hybridization of firefly and Improved Multi-Objective Particle Swarm Optimization algorithm for energy efficient load balancing in Cloud Computing environments

Abstract

Load balancing, in Cloud Computing (CC) environment, is defined as the method of splitting workloads and computing properties. It enables the enterprises to manage workload demands or application demands by distributing the resources among computers, networks or servers. In this research article, a new load balancing algorithm is proposed as a hybrid of firefly and Improved Multi-Objective Particle Swarm Optimization (IMPSO) technique, abbreviated as FIMPSO. This technique deploys Firefly (FF) algorithm to minimize the search space where as the IMPSO technique is implemented to identify the enhanced response. The IMPSO algorithm works by selecting the global best (gbest) particle with a small distance of point to a line. With the application of minimum distance from a point to a line, the gbest particle candidates could be elected. The proposed FIMPSO algorithm achieved effective average load for making and enhanced the essential measures like proper resource usage and response time of the tasks. The simulation outcome showed that the proposed FIMPSO model exhibited an effective performance when compared with other methods. From the simulation outcome, it is understood that the FIMPSO algorithm yielded an effective result with the least average response time of 13.58ms, maximum CPU utilization of 98%, memory utilization of 93%, reliability of 67% and throughput of 72% along with a make span of 148, which was superior to all the other compared methods.