Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing

Abstract Load balancing is a serious problem in cloud computing that makes it challenging to ensure the proper functioning of services contiguous Quality Service, performance assessment, and compliance service contract as demanded from providers (CSP) organizations. The primary objective load map workloads use resources significantly improve performance. falls under class concerns defined "NP-hard" issues due vast solution space. Therefore requires more time predict best possible solution. Few techniques can perhaps generate an ideal polynomial period fix these issues. In previous research, Metaheuristic based strategies have been confirmed accomplish accurate solutions decent for those kinds This paper provides comparative analysis various metaheuristic algorithms on factors i.e., Makespan time, degree imbalance, response data center processing flow resource utilization. simulation results show Meta-heuristic methods, factors. Particle swarm optimization method performs better improving makespan, throughput imbalance.