dc.contributor.author |
Armant, Vincent |
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dc.contributor.author |
De Cauwer, Milan |
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dc.contributor.author |
Brown, Kenneth N. |
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dc.contributor.author |
O'Sullivan, Barry |
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dc.date.accessioned |
2018-01-11T11:22:42Z |
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dc.date.available |
2018-01-11T11:22:42Z |
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dc.date.issued |
2018-01-03 |
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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 |
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dc.identifier.startpage |
89 |
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dc.identifier.endpage |
103 |
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dc.identifier.issn |
0167-739X |
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dc.identifier.uri |
http://hdl.handle.net/10468/5268 |
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dc.identifier.doi |
10.1016/j.future.2017.12.035 |
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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. |
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dc.format.mimetype |
application/pdf |
en |
dc.language.iso |
en |
en |
dc.publisher |
Elsevier B.V. |
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dc.rights |
© 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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dc.rights.uri |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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dc.subject |
Cloud computing |
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dc.subject |
Workload consolidation |
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dc.subject |
Semi-online policies |
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dc.subject |
Stochastic task duration |
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dc.title |
Semi-online task assignment policies for workload consolidation in cloud computing systems |
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dc.type |
Article (peer-reviewed) |
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dc.internal.authorcontactother |
Kenneth Brown, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: k.brown@cs.ucc.ie |
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dc.internal.availability |
Full text available |
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dc.check.info |
Access to this article is restricted until 24 months after publication by request of the publisher. |
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dc.check.date |
2020-01-03 |
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dc.date.updated |
2018-01-11T10:18:34Z |
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dc.description.version |
Accepted Version |
en |
dc.internal.rssid |
421369782 |
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dc.contributor.funder |
Science Foundation Ireland
|
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dc.description.status |
Peer reviewed |
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dc.identifier.journaltitle |
Future Generation Computer Systems |
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dc.internal.copyrightchecked |
Yes |
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dc.internal.licenseacceptance |
Yes |
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dc.internal.IRISemailaddress |
k.brown@cs.ucc.ie |
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dc.relation.project |
info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/
|
en |