Skip to main navigation Skip to search Skip to main content

Energy-aware data transfer tuning

  • SUNY Buffalo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

40 Scopus citations

Abstract

The annual electricity consumed by data transfers in the U.S. is estimated to be 20 Terawatt hours, which translates to around 4 billion U.S. Dollars per year. There has been considerable amount of prior work looking at power management and energy efficiency in hardware and software systems, and more recently in power-aware networking. Despite the growing body of research in power management techniques for the networking infrastructure, there has been no prior work (to the best of our knowledge), focusing on saving energy at the end systems(sender and receiver nodes) during the data transfer. We argue that although network-only approaches are part of the solution, the end-system power management is a key in optimizing energy efficiency of the data transfers, which has been long ignored. In this paper, we analyze various factors that will affect the power consumption in end-to-end data transfers, such as the level of parallelism, concurrency and pipelining. Our results show that significant amount of energy savings can be achieved at the end-systems during data transfer with no or minimal performance penalty.

Original languageEnglish
Title of host publicationProceedings - 14th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2014
PublisherIEEE Computer Society
Pages626-634
Number of pages9
ISBN (Print)9781479927838
DOIs
StatePublished - 2014
Event14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2014 - Chicago, IL, United States
Duration: May 26 2014May 29 2014

Publication series

NameProceedings - 14th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2014

Conference

Conference14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2014
Country/TerritoryUnited States
CityChicago, IL
Period05/26/1405/29/14

Keywords

  • Bigdata
  • Energy efficiency
  • Power modeling
  • Power-aware data transfers
  • Protocol tuning

Fingerprint

Dive into the research topics of 'Energy-aware data transfer tuning'. Together they form a unique fingerprint.

Cite this