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A Novel Based Resource Allocation Method on Cloud Computing Environment Using Hybrid Differential Evolution Algorithm

机译:一种基于云计算环境的基于新的基于资源分配方法,使用混合差分算法

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摘要

Nowadays people are connected to the internet and access various Cloud services and store information. Cloud computing assembles a massive number of virtualized services like infrastructure, platform and software. Cloud computing ensures access to shared resources and common infrastructureand giving services on demand over a network to fulfill dynamic business requests. Implementation of effective multi-objective Resource Allocation is a major issue in cloud computing. Several hybrid optimization algorithms exist to resolve the Resource allocation issues. Genetic Algorithmhybrid with PSO (GAPSO) and Differential Evolution Algorithm hybrid with PSO (DEPSO) are hybrid algorithms of GA and DE, they perform better than ordinary GA and DE. This paper demonstrates the advantage of GAPSO and DEPSO over traditional GA and DE techniques and it exploits multi-objectivetask scheduling using differential evolution with PSO in cloud data centers. Empirical results show that the proposed DEPSO technique improves the efficiency of multi-objective resource allocation. The experimental results prove that DEPSO is able to achieve a better performance than GAPSO.
机译:如今人们已连接到互联网并访问各种云服务和存储信息。云计算汇集了大量的虚拟化服务,如基础架构,平台和软件。云计算可确保访问共享资源和公共基础架构,以满足网络的需求,以满足动态业务请求。实施有效的多目标资源分配是云计算的主要问题。存在几种混合优化算法来解决资源分配问题。具有PSO(Gapso)和差分演进算法的遗传算法用PSO(DEPSO)是GA和DE的混合算法,它们比普通GA和DE更好。本文展示了Gapso和Depso对传统的GA和DE技术的优势,并且它利用了使用云数据中心的PSO差分演进的多靶向调度。经验结果表明,拟议的DEPSO技术提高了多目标资源分配的效率。实验结果证明,DEPSO能够实现比Gapso更好的性能。

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