Improved mutation-based particle swarm optimization for load balancing in cloud data centers

Abstract

Load balancing techniques are a typical NP-hard problem. Currently, many researchers have solved load balancing problem by considering well-known metaheuristic techniques. However, these techniques suffer from one of these issues: premature convergence, poor convergence speed, initially selected random solutions, and stuck in local optima. To handle the issues associated with existing metaheuristic techniques, in this paper, a mutation-based particle swarm optimization based load balancing technique is proposed. The proposed technique has an ability to overcome several issues associated with existing techniques such as premature convergence, poor convergence speed, initially selected random solutions, and stuck in local optima issues. Also, multi-objective fitness function is designed as a minimization problem. Multi-objective fitness function considers energy consumption, makespan, and load imbalance rate parameters. The proposed technique outperforms existing load balancing techniques in terms of makespan, speedup, communication overheads, efficiency, utilization, mean gain time, load imbalance rate, and energy consumption.