ENAGS: energy and network-aware genetic scheduling algorithm on cloud data centers

Soha Rawas, W. Itani, A. Zekri, A. Zaart
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引用次数: 3

Abstract

Cloud computing plays a significant role in today's network computing by delivering virtualized resources as pay-as-you-go services over the Internet. However, the growing demand drastically increases the energy consumption of data centers, which has become a prominent problem. Hence, energy efficient solutions are required to minimize system power consumption and increase the availability of computational resources and obviously reduce the operational expenses. In this paper we present ENAGS (Energy and Network-Aware Genetic Scheduling algorithm) to minimize the energy consumption of servers and reduce the network traffic. The proposed algorithm takes into account communication dependencies among VMs and computational requirements of tasks to improve communication performance and minimize the energy consumption by maximizing the resource utilization. Our experimental results show that the proposed ENAGS algorithm can reduce data center energy consumption as well as network traffic by approximately 38% compared to other placement algorithms.
ENAGS:云数据中心的能源和网络感知遗传调度算法
云计算在当今的网络计算中扮演着重要的角色,它通过Internet将虚拟资源作为即用即付的服务交付。然而,不断增长的需求大大增加了数据中心的能源消耗,这已经成为一个突出的问题。因此,需要高效节能的解决方案,以最大限度地减少系统功耗,提高计算资源的可用性,并明显降低运营费用。在本文中,我们提出了ENAGS (Energy and network - aware Genetic Scheduling algorithm,能量与网络感知遗传调度算法)来最小化服务器的能量消耗和减少网络流量。该算法充分考虑虚拟机之间的通信依赖关系和任务的计算需求,提高通信性能,最大限度地提高资源利用率,使能耗最小化。实验结果表明,与其他放置算法相比,所提出的ENAGS算法可将数据中心能耗和网络流量降低约38%。
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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