Albatli, A, McKee, D orcid.org/0000-0002-9047-7990, Townend, P et al. (2 more authors) (2017) PROV-TE: A Provenance-Driven Diagnostic Framework for Task Eviction in Data Centers. In: 2017 IEEE Third International Conference on Big Data Computing Service and Applications (IEEE BigDataService 2017). IEEE BigDataService 2017, 06-10 Apr 2017, South San Francisco, California, USA. IEEE , pp. 233-242. ISBN 978-1-5090-6318-5
Abstract
Cloud Computing allows users to control substantial computing power for complex data processing, generating huge and complex data. However, the virtual resources requested by users are rarely utilized to their full capacities. To mitigate this, providers often perform over-commitment to maximize profit, which can result in node overloading and consequent task eviction. This paper presents a novel framework that mines the huge and growing historical usage data generated by Cloud data centers to identify the causes of overloads. Provenance modelling is applied to add contextual meaning to the data, and the PROV-TE diagnostic framework provides algorithms to efficiently identify the causality of task eviction. Using simulation to reflect real world scenarios, our results demonstrate a precision and recall of the diagnostic algorithms of 83% and 90% respectively. This demonstrates a high level of accuracy of the identification of causes.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Big Data; Data Centers; Cyberinfrastructure; Cloud Computing; Overcommitment; Overload; Provenance; PROV; Simulation; Distributed Systems; Data models; Computational modeling; Google; Analytical models; Distributed databases; Standards |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Feb 2017 12:44 |
Last Modified: | 16 Jan 2018 20:15 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/BigDataService.2017.34 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:112765 |