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Conference Paper: Scaling social media applications into geo-distributed clouds
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TitleScaling social media applications into geo-distributed clouds
 
AuthorsWu, Y1
Wu, C1
Li, B3
Zhang, L1
Li, Z2
Lau, FCM1
 
KeywordsCloud services
Data centers
Demand prediction
Distribution algorithms
Dynamic content
 
Issue Date2012
 
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000359
 
CitationThe 31st Annual IEEE International Conference on Computer Communications (IEEE INFOCOM 2012), Orlando, FL., 25-30 March 2012. In IEEE Infocom Proceedings, 2012, p. 684-692 [How to Cite?]
DOI: http://dx.doi.org/10.1109/INFCOM.2012.6195813
 
AbstractFederation of geo-distributed cloud services is a trend in cloud computing which, by spanning multiple data centers at different geographical locations, can provide a cloud platform with much larger capacities. Such a geo-distributed cloud is ideal for supporting large-scale social media streaming applications (e.g., YouTube-like sites) with dynamic contents and demands, owing to its abundant on-demand storage/bandwidth capacities and geographical proximity to different groups of users. Although promising, its realization presents challenges on how to efficiently store and migrate contents among different cloud sites (i.e. data centers), and to distribute user requests to the appropriate sites for timely responses at modest costs. These challenges escalate when we consider the persistently increasing contents and volatile user behaviors in a social media application. By exploiting social influences among users, this paper proposes efficient proactive algorithms for dynamic, optimal scaling of a social media application in a geo-distributed cloud. Our key contribution is an online content migration and request distribution algorithm with the following features: (1) future demand prediction by novelly characterizing social influences among the users in a simple but effective epidemic model; (2) oneshot optimal content migration and request distribution based on efficient optimization algorithms to address the predicted demand, and (3) a Δ(t)-step look-ahead mechanism to adjust the one-shot optimization results towards the offline optimum. We verify the effectiveness of our algorithm using solid theoretical analysis, as well as large-scale experiments under dynamic realistic settings on a home-built cloud platform. © 2012 IEEE.
 
DescriptionTS51: Cloud/Grid computing and networks 3
 
ISBN978-1-4673-0775-8
 
ISSN0743-166X
2013 SCImago Journal Rankings: 1.482
 
DOIhttp://dx.doi.org/10.1109/INFCOM.2012.6195813
 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorWu, Y
 
dc.contributor.authorWu, C
 
dc.contributor.authorLi, B
 
dc.contributor.authorZhang, L
 
dc.contributor.authorLi, Z
 
dc.contributor.authorLau, FCM
 
dc.date.accessioned2012-06-26T06:32:56Z
 
dc.date.available2012-06-26T06:32:56Z
 
dc.date.issued2012
 
dc.description.abstractFederation of geo-distributed cloud services is a trend in cloud computing which, by spanning multiple data centers at different geographical locations, can provide a cloud platform with much larger capacities. Such a geo-distributed cloud is ideal for supporting large-scale social media streaming applications (e.g., YouTube-like sites) with dynamic contents and demands, owing to its abundant on-demand storage/bandwidth capacities and geographical proximity to different groups of users. Although promising, its realization presents challenges on how to efficiently store and migrate contents among different cloud sites (i.e. data centers), and to distribute user requests to the appropriate sites for timely responses at modest costs. These challenges escalate when we consider the persistently increasing contents and volatile user behaviors in a social media application. By exploiting social influences among users, this paper proposes efficient proactive algorithms for dynamic, optimal scaling of a social media application in a geo-distributed cloud. Our key contribution is an online content migration and request distribution algorithm with the following features: (1) future demand prediction by novelly characterizing social influences among the users in a simple but effective epidemic model; (2) oneshot optimal content migration and request distribution based on efficient optimization algorithms to address the predicted demand, and (3) a Δ(t)-step look-ahead mechanism to adjust the one-shot optimization results towards the offline optimum. We verify the effectiveness of our algorithm using solid theoretical analysis, as well as large-scale experiments under dynamic realistic settings on a home-built cloud platform. © 2012 IEEE.
 
dc.description.naturepublished_or_final_version
 
dc.descriptionTS51: Cloud/Grid computing and networks 3
 
dc.description.otherThe 31st Annual IEEE International Conference on Computer Communications (IEEE INFOCOM 2012), Orlando, FL., 25-30 March 2012. In IEEE Infocom Proceedings, 2012, p. 684-692
 
dc.identifier.citationThe 31st Annual IEEE International Conference on Computer Communications (IEEE INFOCOM 2012), Orlando, FL., 25-30 March 2012. In IEEE Infocom Proceedings, 2012, p. 684-692 [How to Cite?]
DOI: http://dx.doi.org/10.1109/INFCOM.2012.6195813
 
dc.identifier.doihttp://dx.doi.org/10.1109/INFCOM.2012.6195813
 
dc.identifier.epage692
 
dc.identifier.hkuros202422
 
dc.identifier.isbn978-1-4673-0775-8
 
dc.identifier.issn0743-166X
2013 SCImago Journal Rankings: 1.482
 
dc.identifier.scopuseid_2-s2.0-84861634944
 
dc.identifier.spage684
 
dc.identifier.urihttp://hdl.handle.net/10722/152049
 
dc.languageeng
 
dc.publisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000359
 
dc.publisher.placeUnited States
 
dc.relation.ispartofIEEE Infocom Proceedings
 
dc.relation.referencesReferences in Scopus
 
dc.rightsIEEE Infocom Proceedings. Copyright © IEEE Computer Society.
 
dc.rights©2012 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.subjectCloud services
 
dc.subjectData centers
 
dc.subjectDemand prediction
 
dc.subjectDistribution algorithms
 
dc.subjectDynamic content
 
dc.titleScaling social media applications into geo-distributed clouds
 
dc.typeConference_Paper
 
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<subject>Cloud services</subject>
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Author Affiliations
  1. The University of Hong Kong
  2. University of Calgary
  3. Hong Kong University of Science and Technology