Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/221432

Download PDF (Accepted manuscript) (Adobe Acrobat PDF, -1 bytes)

Title

An energy consumption model and analysis tool for cloud computing environments

Author(s)

Chen, FeiFei;  Schneider, Jean-Guy;  Yang, Yun;  Grundy, John;  He, Qiang

Abstract

Cloud computing delivers computing as a utility to users worldwide. A consequence of this model is that cloud data centres have high deployment and operational costs, as well as significant carbon footprints for the environment. We need to develop Green Cloud Computing (Gee) solutions that reduce these deployment and operational costs and thus save energy and reduce adverse environmental impacts. In order to achieve this Objective, a thorough understanding of the energy consumption patterns in complex Cloud environments is needed. We present a new energy consumption model and associated analysis tool for Cloud computing environments. We measure energy consumption in Cloud environments based on different runtime tasks. Empirical analysis of the correlation of energy consumption and Cloud data and computational tasks, as well as system performance, will be investigated based on our energy consumption model and analysis tool. Our research results can be integrated into Cloud systems to monitor energy consumption and support static or dynamic system-level optimisation.

Publication type

Conference paper

Research centre

Swinburne University of Technology. Faculty of Information and Communication Technologies

Source

Proceedings of the First International Workshop on Green and Sustainable Software Engineering (GREENS), Zurich, Switzerland, 03 June 2012, pp. 45-50

Publication year

2012

Keyword(s)

Cloud computing;  Energy consumption;  Green computing;  Performance analysis

Publisher

IEEE

ISBN

9781467318327, 1467318329

Publisher URL

http://dx.doi.org/10.1109/GREENS.2012.6224255

Copyright

Copyright © 2012 IEEE. Published version of this paper reproduced here in accordance with the copyright policy of the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Research Projects

Cost effective storage of massive intermediate data in cloud computing applications, Australian Research Council grant number DP110101340

Full text

Peer reviewed