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 language | English |
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Journal | 2015 IEEE Conference on Technologies for Sustainability, SusTech 2015 |
Pages (from-to) | 47-52 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 30 Oct 2015 |
Externally published | Yes |
Event | 3rd Annual IEEE Conference on Technologies for Sustainability, SusTech 2015 - Ogden, United States Duration: 30 Jul 2015 → 1 Aug 2015 |
Conference
Conference | 3rd Annual IEEE Conference on Technologies for Sustainability, SusTech 2015 |
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Country/Territory | United States |
City | Ogden |
Period | 30/07/2015 → 01/08/2015 |
Sponsor | IEEE 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