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Conference Paper: Online algorithms for uploading deferrable big data to the cloud

TitleOnline algorithms for uploading deferrable big data to the cloud
Authors
Issue Date2014
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000359
Citation
The 33rd IEEE Conference on Computer Communications (IEEE INFOCOM 2014), Toronto, ON., 27 April-2 May 2014. In IEEE Infocom Proceedings, 2014, p. 2022-2030 How to Cite?
AbstractThis work studies how to minimize the bandwidth cost for uploading deferral big data to a cloud computing platform, for processing by a MapReduce framework, assuming the Internet service provider (ISP) adopts the MAX contract pricing scheme. We first analyze the single ISP case and then generalize to the MapReduce framework over a cloud platform. In the former, we design a Heuristic Smoothing algorithm whose worst-case competitive ratio is proved to fall between 2-1/(D+1) and 2(1 - 1/e), where D is the maximum tolerable delay. In the latter, we employ the Heuristic Smoothing algorithm as a building block, and design an efficient distributed randomized online algorithm, achieving a constant expected competitive ratio. The Heuristic Smoothing algorithm is shown to outperform the best known algorithm in the literature through both theoretical analysis and empirical studies. The efficacy of the randomized online algorithm is also verified through simulation studies. © 2014 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/201094
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorZhang, Len_US
dc.contributor.authorLi, Zen_US
dc.contributor.authorWu, Cen_US
dc.contributor.authorChen, Men_US
dc.date.accessioned2014-08-21T07:13:33Z-
dc.date.available2014-08-21T07:13:33Z-
dc.date.issued2014en_US
dc.identifier.citationThe 33rd IEEE Conference on Computer Communications (IEEE INFOCOM 2014), Toronto, ON., 27 April-2 May 2014. In IEEE Infocom Proceedings, 2014, p. 2022-2030en_US
dc.identifier.isbn978-14799-3360-0-
dc.identifier.issn0743-166X-
dc.identifier.urihttp://hdl.handle.net/10722/201094-
dc.description.abstractThis work studies how to minimize the bandwidth cost for uploading deferral big data to a cloud computing platform, for processing by a MapReduce framework, assuming the Internet service provider (ISP) adopts the MAX contract pricing scheme. We first analyze the single ISP case and then generalize to the MapReduce framework over a cloud platform. In the former, we design a Heuristic Smoothing algorithm whose worst-case competitive ratio is proved to fall between 2-1/(D+1) and 2(1 - 1/e), where D is the maximum tolerable delay. In the latter, we employ the Heuristic Smoothing algorithm as a building block, and design an efficient distributed randomized online algorithm, achieving a constant expected competitive ratio. The Heuristic Smoothing algorithm is shown to outperform the best known algorithm in the literature through both theoretical analysis and empirical studies. The efficacy of the randomized online algorithm is also verified through simulation studies. © 2014 IEEE.-
dc.languageengen_US
dc.publisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000359-
dc.relation.ispartofIEEE Infocom Proceedingsen_US
dc.rightsIEEE Infocom. Proceedings. Copyright © IEEE Computer Society.-
dc.rights©2014 IEEE. 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.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleOnline algorithms for uploading deferrable big data to the clouden_US
dc.typeConference_Paperen_US
dc.identifier.emailWu, C: cwu@cs.hku.hken_US
dc.identifier.authorityWu, C=rp01397en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/INFOCOM.2014.6848143-
dc.identifier.scopuseid_2-s2.0-84904437872-
dc.identifier.hkuros232123en_US
dc.identifier.spage2022-
dc.identifier.epage2030-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 140822-

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