An efficient load balancing using seven stone game optimization in cloud computing. (6th January 2021)

• Record Type: Journal Article
• Authors: Karthikeyan, Periyasami
• Is Part Of: Software, practice & experience. Volume 51:Number 6(2021)
• Page Start: 1242
• Page End: 1258
• Publication Date: 2021-01-06
• Subjects: cloud computing -- genetic algorithm -- load balancing -- particle swarm optimization -- seven stone game optimization -- simulated annealing -- tabu search; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2954
• Languages: English
• ISSNs: 0038-0644

Abstract

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.