Semi-online task assignment policies for workload consolidation in cloud computing systems

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dc.contributor.author Armant, Vincent
dc.contributor.author De Cauwer, Milan
dc.contributor.author Brown, Kenneth N.
dc.contributor.author O'Sullivan, Barry
dc.date.accessioned 2018-01-11T11:22:42Z
dc.date.available 2018-01-11T11:22:42Z
dc.date.issued 2018-01-03
dc.identifier.citation Armant, V., De Cauwer, M. Brown, K. N. and O'Sullivan, B. (2018) 'Semi-online task assignment policies for workload consolidation in cloud computing systems', Future Generation Computer Systems, 82, pp. 89-103. doi:10.1016/j.future.2017.12.035 en
dc.identifier.volume 82
dc.identifier.startpage 89
dc.identifier.endpage 103
dc.identifier.issn 0167-739X
dc.identifier.uri http://hdl.handle.net/10468/5268
dc.identifier.doi 10.1016/j.future.2017.12.035
dc.description.abstract Satisfying on-demand access to cloud computing infrastructures under quality-of-service constraints while minimising the wastage of resources is an important challenge in data centre resource management. In this paper we tackle this challenge in a semi-online workload management system allocating tasks with uncertain duration to physical servers. Our semi-online framework, based on a bin packing approach, allows us to gather information on incoming tasks during a short time window before deciding on their assignments. Our contributions are as follows: (i) we propose a formal framework capturing the semi-online consolidation problem; (ii) we propose a new dynamic and real-time allocation algorithm based on the incremental merging of bins; and (iii) an adaptation of standard bin packing heuristics with a local search algorithm for the semi-online context considered here. We provide a systematic study of the impact of varying time-period size and varying the degrees of uncertainty on the duration of incoming tasks. The policies are compared in terms of solution quality and solving time on a data-set extracted from a real-world cluster trace. Our results show that, around periods of high demand, our best policy saves up to 40% of the resources compared to the other polices, and is robust to uncertainty in the task durations. Finally, we show that small increases in the allowable time window allows a significant improvement, but that larger time windows do not necessarily improve resource usage for real world data sets. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Elsevier B.V. en
dc.rights © 2018, Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license. en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ en
dc.subject Cloud computing en
dc.subject Workload consolidation en
dc.subject Semi-online policies en
dc.subject Stochastic task duration en
dc.title Semi-online task assignment policies for workload consolidation in cloud computing systems en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Kenneth Brown, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: k.brown@cs.ucc.ie en
dc.internal.availability Full text available en
dc.check.info Access to this article is restricted until 24 months after publication by request of the publisher. en
dc.check.date 2020-01-03
dc.date.updated 2018-01-11T10:18:34Z
dc.description.version Accepted Version en
dc.internal.rssid 421369782
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Future Generation Computer Systems en
dc.internal.copyrightchecked Yes en
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress k.brown@cs.ucc.ie en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ en


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© 2018, Elsevier B.V. All rights reserved.  This manuscript version is made available under the CC-BY-NC-ND 4.0 license. Except where otherwise noted, this item's license is described as © 2018, Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.
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