An efficient load balancing using seven stone game optimization in cloud computing

Abstract

Cloud computing offers massive processing power to cloud client to solve the scientific, financial forecasting, and weather forecasting applications. The process of distributing to the load to the different cloud service providers is a complex problem. Cloud service providers have different types of virtual machines with different computing power types in multi-layered architectures. Various optimization works have been proposed to tackle the load balancing problem in cloud service providers. Improving performance in load balancing is a cumbersome task. Seven stone game optimization (SSGO) is designed based on the south Indian seven stone game workflow. The proposed method's foremost ambition is to reduce makespan time and maximize cloud service providers' utilization. The proposed method was simulated, and results demonstrate that minimizes the makespan time and maximizes the resource utilization than the particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), and Tabu search (TS). The experimental results show that the SSGO provides 4% more resource utilization than PSO, 5% more than GA, and 7% more than SA and 10% more than TS.

Hybrid dingo and whale optimization algorithm‐based optimal load balancing for cloud computing environment

“…This method of RDA is attained using the properties of tasks' execution time, memory consumption, utilization of CPU, VM load balancing, and storage capacity. Karthikeyan 31 contributed a load balancing strategy using seven stone game optimization (SSGOLB) for maximizing the utilization rate of cloud service providers with minimized time of makespan. This SSGOLB adopted a fitness function using maximum deviation in incoming workload, SD in workload, throughput, and response time.…”