Load Balancing in Cloud Computing using Mutation Based Particle Swarm Optimization

Abstract

Cloud computing has emerged as a technology that grease tasks by the dynamic allocation of virtual machines. Users pay for resources based on their demand. A cloud provider has to face many challenges. One out of the essential problem is load balancing, which suffers from many issues like premature convergence, reduced convergence speed, at first chosen random solutions, and stuck in native optima. The proposed method considered the MakeSpan parameters to handle the problem related to existing met heuristic techniques. The proposed method focuses on the mutation-based Particle Swarm algorithm to balance load among the data centers. Here an efficient load balancing algorithm is developed to minimize performance parameters like MakeSpan time and improve the fitness function in cloud computing.