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An efficient load balancing using seven stone game optimization in cloud computing
Software: Practice and Experience ( IF 3.5 ) Pub Date : 2021-01-06 , DOI: 10.1002/spe.2954
Periyasami Karthikeyan 1
Affiliation  

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.

中文翻译:

在云计算中使用七种石头游戏优化的有效负载平衡

云计算为云客户端提供了强大的处理能力,可以解决科学,财务预测和天气预报应用程序。将负载分配给不同的云服务提供商的过程是一个复杂的问题。云服务提供商在多层架构中具有不同类型的虚拟机,这些虚拟机具有不同的计算能力类型。已经提出了各种优化工作来解决云服务提供商中的负载平衡问题。改善负载平衡的性能是一项繁琐的任务。七石游戏优化(SSGO)是基于南印度七石游戏工作流程而设计的。所提出的方法的最大目标是减少制造时间并最大化云服务提供商的利用率。对提出的方法进行了仿真,结果表明,与粒子群优化(PSO),遗传算法(GA),模拟退火(SA)和禁忌搜索(TS)相比,该方法可以最大程度地缩短制造时间并最大程度地利用资源。实验结果表明,SSGO比PSO的资源利用率高4%,比GA的资源利用率高5%,比SA的资源利用率高7%,比TS的资源利用率高10%。
更新日期:2021-01-06
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