GreenDCN: A general framework for achieving energy efficiency in data center networks

Lin Wang, Fa Zhang, Jordi Arjona Aroca, Athanasios V. Vasilakos, Kai Zheng, Chenying Hou, Dan Li, Zhiyong Liu

Research output: Contribution to journalArticlepeer-review

161 Citations (Scopus)

Abstract

The popularization of cloud computing has raised concerns over the energy consumption that takes place in data centers. In addition to the energy consumed by servers, the energy consumed by large numbers of network devices emerges as a significant problem. Existing work on energy-efficient data center networking primarily focuses on traffic engineering, which is usually adapted from traditional networks. We propose a new framework to embrace the new opportunities brought by combining some special features of data centers with traffic engineering. Based on this framework, we characterize the problem of achieving energy efficiency with a time-aware model, and we prove its NP-hardness with a solution that has two steps. First, we solve the problem of assigning virtual machines (VM) to servers to reduce the amount of traffic and to generate favorable conditions for traffic engineering. The solution reached for this problem is based on three essential principles that we propose. Second, we reduce the number of active switches and balance traffic flows, depending on the relation between power consumption and routing, to achieve energy conservation. Experimental results confirm that, by using this framework, we can achieve up to 50 percent energy savings. We also provide a comprehensive discussion on the scalability and practicability of the framework.

Original languageEnglish
Article number140102
Pages (from-to)4-15
Number of pages12
JournalIEEE Journal on Selected Areas in Communications
Volume32
Issue number1
DOIs
Publication statusPublished - Jan 2014
Externally publishedYes

Keywords

  • Data center networks
  • Energy efficiency
  • Traffic engineering
  • Virtual machine assignment

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