<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>

COPY SCRIPT

For further information contact us at helpdesk@openaire.eu

On allocation policies for power and performance

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Other literature type 01 Oct 2010Embargo end date: 01 Jan 2011Publisher:IEEEJournal:2010 11th IEEE/ACM International Conference on Grid Computing

Authors: Michele Mazzucco; Dmytro Dyachuk;

doi: 10.1109/grid.2010.5697986 , 10.48550/arxiv.1102.3272

arXiv: http://arxiv.org/abs/1102.3272

On allocation policies for power and performance

- Summary
- Subjects
- Metrics

Abstract

With the increasing popularity of Internet-based services and applications, power efficiency is becoming a major concern for data center operators, as high electricity consumption not only increases greenhouse gas emissions, but also increases the cost of running the server farm itself. In this paper we address the problem of maximizing the revenue of a service provider by means of dynamic allocation policies that run the minimum amount of servers necessary to meet user's requirements in terms of performance. The results of several experiments executed using Wikipedia traces are described, showing that the proposed schemes work well, even if the workload is non-stationary. Since any resource allocation policy requires the use of forecasting mechanisms, various schemes allowing compensating errors in the load forecasts are presented and evaluated.

8 pages, 11 figures, 2010 11th IEEE/ACM International Conference on Grid Computing (GRID), pp 313 - 320 (E2GC2-2010 workshop)

Related Organizations

University of Tartu
Estonia
University of Saskatchewan
Canada

Keywords

Performance (cs.PF), FOS: Computer and information sciences, Computer Science - Performance, Computer Science - Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC)

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	11
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%