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An efficient load balancing using seven stone game optimization in cloud computing
Author(s) -
Karthikeyan Periyasami
Publication year - 2021
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2954
Subject(s) - cloud computing , computer science , job shop scheduling , particle swarm optimization , load balancing (electrical power) , tabu search , distributed computing , workflow , virtual machine , cloud service provider , simulated annealing , service provider , scheduling (production processes) , mathematical optimization , service (business) , database , artificial intelligence , algorithm , computer network , cloud computing security , operating system , routing (electronic design automation) , geometry , mathematics , economy , economics , grid
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.