A Learning Automata Based Dynamic Resource Provisioning in Cloud Computing Environments

H. Qavami, S. Jamali, M. Akbari, B. Javadi
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引用次数: 5

Abstract

Cloud computing provides more reliable and flexible access to IT resources, on-demand and self-service service request are some key advantages of it. Managing up-layer cloud services efficiently, while promising those advantages and SLA, motivates the challenge of provisioning and allocating resource on-demand in infrastructure layer, in response to dynamic workloads. Studies mostly have been focused on managing these demands in the physical layer and few in the application layer. This paper focuses on resource allocation method in application level that allocates an appropriate number of virtual machines to an application which requires a dynamic amount of resources. A Learning Automata based approach has been chosen to implement the method. Experimental results demonstrate that the proposed technique offers more cost effective resource provisioning approach while provisions enough resource for applications.
云计算环境下基于学习自动机的动态资源配置
云计算提供了更可靠、更灵活的IT资源访问,按需服务和自助服务请求是它的一些关键优势。有效地管理上层云服务,同时保证这些优势和SLA,激发了在基础设施层按需供应和分配资源的挑战,以响应动态工作负载。研究大多集中在物理层的需求管理上,应用层的需求管理很少。本文主要研究应用程序级的资源分配方法,该方法为需要动态资源的应用程序分配适当数量的虚拟机。选择了一种基于学习自动机的方法来实现该方法。实验结果表明,该技术在为应用提供足够资源的同时,提供了一种更具成本效益的资源配置方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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