FPSO-GA: A Fuzzy Metaheuristic Load Balancing Algorithm to Reduce Energy Consumption in Cloud Networks

Load balancing in the cloud is a strategy that assures that the overall performance of large-scale computing systems can be improved by ensuring a uniform allocation of local workloads among computing system components. Many studies and algorithms in cloud computing load balancing, task scheduling, and workflow scheduling have been proposed so far. However, because of the enormous number of competing criteria and the different nature of dynamic Task allocation to heterogeneous resources that deal with scheduling, it is nearly difficult to identify an optimal solution for every scheduling problem at any given time. One of the scheduling ways is to apply meta-heuristic techniques, which attempt to discover a near-optimal solution in a predictable amount of time while demonstrating exceptional performance on the goal task. We develop a hybrid Fuzzy Particle Swarm Optimization Genetic Algorithm (FPSO-GA) method that combines a fuzzy particle swarm optimization method and a genetic algorithm in this study.