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Electronics 2016, 5(4), 90; doi:10.3390/electronics5040090

Characterizing Energy per Job in Cloud Applications

Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
These authors contributed equally to this work.
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Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Bein
Received: 31 July 2016 / Revised: 28 November 2016 / Accepted: 2 December 2016 / Published: 12 December 2016
(This article belongs to the Special Issue Energy Saving in Data Centers)

Abstract

Energy efficiency is a major research focus in sustainable development and is becoming even more critical in information technology (IT) with the introduction of new technologies, such as cloud computing and big data, that attract more business users and generate more data to be processed. While many proposals have been presented to optimize power consumption at a system level, the increasing heterogeneity of current workloads requires a finer analysis in the application level to enable adaptive behaviors and in order to reduce the global energy usage. In this work, we focus on batch applications running on virtual machines in the context of data centers. We analyze the application characteristics, model their energy consumption and quantify the energy per job. The analysis focuses on evaluating the efficiency of applications in terms of performance and energy consumed per job, in particular when shared resources are used and the hosts on which the virtual machines are running are heterogeneous in terms of energy profiles, with the aim of identifying the best combinations in the use of resources. View Full-Text
Keywords: cloud computing; energy consumption models; energy efficiency; batch applications cloud computing; energy consumption models; energy efficiency; batch applications
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ho, T.T.N.; Gribaudo, M.; Pernici, B. Characterizing Energy per Job in Cloud Applications. Electronics 2016, 5, 90.

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