4.6 Article

Balanced Energy-Aware and Fault-Tolerant Data Center Scheduling

期刊

SENSORS
卷 22, 期 4, 页码 -

出版社

MDPI
DOI: 10.3390/s22041482

关键词

cloud computing; networks; data center; fault tolerance; energy efficiency

向作者/读者索取更多资源

The objective of this study is to improve the energy efficiency of data center networks while maintaining fault tolerance. The proposed Energy-Aware Fault-Tolerant (EAFT) approach is compared and analyzed with other energy efficient resource scheduling techniques. The study shows that there is a tradeoff between energy efficiency and fault tolerance, and it is possible to achieve a balance between the two.
Fault tolerance, performance, and throughput have been major areas of research and development since the evolution of large-scale networks. Internet-based applications are rapidly growing, including large-scale computations, search engines, high-definition video streaming, e-commerce, and video on demand. In recent years, energy efficiency and fault tolerance have gained significant importance in data center networks and various studies directed the attention towards green computing. Data centers consume a huge amount of energy and various architectures and techniques have been proposed to improve the energy efficiency of data centers. However, there is a tradeoff between energy efficiency and fault tolerance. The objective of this study is to highlight a better tradeoff between the two extremes: (a) high energy efficiency and (b) ensuring high availability through fault tolerance and redundancy. The main objective of the proposed Energy-Aware Fault-Tolerant (EAFT) approach is to keep one level of redundancy for fault tolerance while scheduling resources for energy efficiency. The resultant energy-efficient data center network provides availability as well as fault tolerance at reduced operating cost. The main contributions of this article are: (a) we propose an Energy-Aware Fault-Tolerant (EAFT) data center network scheduler; (b) we compare EAFT with energy efficient resource scheduling techniques to provide analysis of parameters such as, workload distribution, average task per servers, and energy consumption; and (c) we highlight effects of energy efficiency techniques on the network performance of the data center.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据