A data-value-driven adaptation framework for energy efficiency for data intensive applications in clouds

Thi Thao Nguyen Ho, Barbara Pernici

Research output: Contribution to journalConference article in JournalResearchpeer-review

11 Citations (Scopus)

Abstract

The emerging of cloud computing and Big Data has been presenting to the world both grand opportunities and challenges. However, the increasing trend in energy consumption in clouds due to the fast growing quantity of data to be transmitted and processed has made cloud computing, together with Big Data phenomenon, becoming the dominant contributor in energy consumption, and consequently in CO2 emission. In this paper, we propose an adaptation framework for data-intensive applications aiming to improve energy efficiency. The adaptation mechanism is driven by the data value extracted from datasets or data streams of the applications. Our main contribution lies in the proposal of treating large amount of data according to their value, i.e., their level of importance.

Original languageEnglish
Journal2015 IEEE Conference on Technologies for Sustainability, SusTech 2015
Pages (from-to)47-52
Number of pages6
DOIs
Publication statusPublished - 30 Oct 2015
Externally publishedYes
Event3rd Annual IEEE Conference on Technologies for Sustainability, SusTech 2015 - Ogden, United States
Duration: 30 Jul 20151 Aug 2015

Conference

Conference3rd Annual IEEE Conference on Technologies for Sustainability, SusTech 2015
Country/TerritoryUnited States
CityOgden
Period30/07/201501/08/2015
SponsorIEEE Oregon Section, IEEE Region 6 (Western USA), IEEE Utah Section

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

  • Big Data
  • Cloud computing
  • data value
  • energy efficiency
  • MapReduce

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