Energy Efficient Data Center in Cloud Computing

V. Yadav, Pooja Malik, Adarsh Kumar, G. Sahoo
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引用次数: 4

Abstract

In fact, Gartner projected global revenue for cloud computing to reach almost $150 billion by 2014. However, the 2011 market is already approximately $68 billion globally. With increase in web technologies and internet, a proportional increase in cloud computing technologies has been cited. Cloud computing has been emerging as a flexible and powerful computational architecture to offer ubiquitous services to users. A variety of hardware and software resources are integrated together as a resource pool, the software is no longer resided in a single hardware environment, it is utilized according to the schedule of the resource pool for optimized resource utilization. The optimization of energy consumption in the cloud computing environment is the question how to use various energy conservation strategies to efficiently allocate resources. The need of different resources in cloud environment is unpredictable. It is observed that load management in cloud is utmost needed in order to provide QoS (Quality of Service). The jobs on over-loaded physical machine are shifted to under-loaded physical machine and turning the idle machine off, in order to provide green cloud. For energy optimization, DVFS and Power-Nap are good strategies. As good amount of this energy is wasted in idle systems: in typical deployments, server utilization is below 30%, but idle servers still consume 60% of their peak power. In this paper, we have proposed an algorithm for energy optimization having the constraint QoS and SLA.
云计算中的节能数据中心
事实上,高德纳预测,到2014年,云计算的全球收入将达到近1500亿美元。然而,2011年全球游戏市场规模已经达到680亿美元。随着网络技术和互联网的发展,云计算技术也相应增加。云计算已经成为一种灵活而强大的计算体系结构,可以为用户提供无处不在的服务。将各种硬件和软件资源集成在一起,形成一个资源池,软件不再驻留在单一的硬件环境中,而是按照资源池的调度进行利用,实现资源的优化利用。云计算环境下的能耗优化问题是如何利用各种节能策略有效地分配资源。云环境中对不同资源的需求是不可预测的。为了提供服务质量(QoS),云中的负载管理是最需要的。将负载过重的物理机上的作业转移到负载不足的物理机上,并关闭空闲的物理机上,以提供绿色云。对于能量优化,DVFS和Power-Nap是很好的策略。大量的能量被浪费在空闲系统中:在典型部署中,服务器利用率低于30%,但空闲服务器仍然消耗其峰值功率的60%。本文提出了一种具有QoS和SLA约束的能量优化算法。
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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