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Conference Paper: Moving big data to the cloud

TitleMoving big data to the cloud
Authors
KeywordsCompetitive ratio
Computation paradigms
Data aggregation
Fixed horizons
Geographical locations
On-line algorithms
Resource access
Transmitting data
Issue Date2013
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000359
Citation
The 32nd IEEE Conference on Computer Communications (IEEE INFOCOM 2013), Turin, Italy, 14-19 April 2013. In IEEE Infocom Proceedings, 2013, p. 405-409 How to Cite?
AbstractCloud computing, rapidly emerging as a new computation paradigm, provides agile and scalable resource access in a utility-like fashion, especially for the processing of big data. An important open issue here is how to efficiently move the data, from different geographical locations over time, into a cloud for effective processing. The de facto approach of hard drive shipping is not flexible, nor secure. This work studies timely, cost-minimizing upload of massive, dynamically-generated, geo-dispersed data into the cloud, for processing using a MapReduce-like framework. Targeting at a cloud encompassing disparate data centers, we model a cost-minimizing data migration problem, and propose two online algorithms, for optimizing at any given time the choice of the data center for data aggregation and processing, as well as the routes for transmitting data there. The first is an online lazy migration (OLM) algorithm achieving a competitive ratio of as low as 2.55, under typical system settings. The second is a randomized fixed horizon control (RFHC) algorithm achieving a competitive ratio of 1+ 1/l+1 κ/λ with a lookahead window of l, where κ and λ are system parameters of similar magnitude. © 2013 IEEE.
DescriptionMini-Conference - IEEE INFOCOM 2013
Persistent Identifierhttp://hdl.handle.net/10722/186478
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorZhang, Len_US
dc.contributor.authorWu, Cen_US
dc.contributor.authorLi, Zen_US
dc.contributor.authorGuo, Cen_US
dc.contributor.authorChen, Men_US
dc.contributor.authorLau, FCMen_US
dc.date.accessioned2013-08-20T12:11:09Z-
dc.date.available2013-08-20T12:11:09Z-
dc.date.issued2013en_US
dc.identifier.citationThe 32nd IEEE Conference on Computer Communications (IEEE INFOCOM 2013), Turin, Italy, 14-19 April 2013. In IEEE Infocom Proceedings, 2013, p. 405-409en_US
dc.identifier.isbn978-1-4673-5946-7-
dc.identifier.issn0743-166X-
dc.identifier.urihttp://hdl.handle.net/10722/186478-
dc.descriptionMini-Conference - IEEE INFOCOM 2013-
dc.description.abstractCloud computing, rapidly emerging as a new computation paradigm, provides agile and scalable resource access in a utility-like fashion, especially for the processing of big data. An important open issue here is how to efficiently move the data, from different geographical locations over time, into a cloud for effective processing. The de facto approach of hard drive shipping is not flexible, nor secure. This work studies timely, cost-minimizing upload of massive, dynamically-generated, geo-dispersed data into the cloud, for processing using a MapReduce-like framework. Targeting at a cloud encompassing disparate data centers, we model a cost-minimizing data migration problem, and propose two online algorithms, for optimizing at any given time the choice of the data center for data aggregation and processing, as well as the routes for transmitting data there. The first is an online lazy migration (OLM) algorithm achieving a competitive ratio of as low as 2.55, under typical system settings. The second is a randomized fixed horizon control (RFHC) algorithm achieving a competitive ratio of 1+ 1/l+1 κ/λ with a lookahead window of l, where κ and λ are system parameters of similar magnitude. © 2013 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©2013 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.subjectCompetitive ratio-
dc.subjectComputation paradigms-
dc.subjectData aggregation-
dc.subjectFixed horizons-
dc.subjectGeographical locations-
dc.subjectOn-line algorithms-
dc.subjectResource access-
dc.subjectTransmitting data-
dc.titleMoving big data to the clouden_US
dc.typeConference_Paperen_US
dc.identifier.emailWu, C: cwu@cs.hku.hken_US
dc.identifier.emailLau, FCM: fcmlau@cs.hku.hken_US
dc.identifier.authorityWu, C=rp01397en_US
dc.identifier.authorityLau, FCM=rp00221en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/INFCOM.2013.6566804-
dc.identifier.scopuseid_2-s2.0-84883056824-
dc.identifier.hkuros217642en_US
dc.identifier.spage405-
dc.identifier.epage409-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 140822-

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