Conference Paper: Scaling social media applications into geo-distributed clouds

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Publisher Website: 10.1109/INFCOM.2012.6195813
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Title	Scaling social media applications into geo-distributed clouds
Authors	Wu, Y1 Wu, C1 Li, B3 Zhang, L1 Li, Z2 Lau, FCM1
Keywords	Cloud services Data centers Demand prediction Distribution algorithms Dynamic content
Issue Date	2012
Publisher	IEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000359
Citation	The 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
Abstract	Federation 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.
Description	TS51: Cloud/Grid computing and networks 3
ISBN	978-1-4673-0775-8
ISSN	0743-166X 2011 SCImago Journal Rankings: 0.047
DOI	http://dx.doi.org/10.1109/INFCOM.2012.6195813
References	References in Scopus

DC Field	Value
dc.contributor.author	Wu, Y
dc.contributor.author	Wu, C
dc.contributor.author	Li, B
dc.contributor.author	Zhang, L
dc.contributor.author	Li, Z
dc.contributor.author	Lau, FCM
dc.date.accessioned	2012-06-26T06:32:56Z
dc.date.available	2012-06-26T06:32:56Z
dc.date.issued	2012
dc.description.abstract	Federation 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.nature	published_or_final_version
dc.description	TS51: Cloud/Grid computing and networks 3
dc.description.other	The 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.citation	The 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.doi	http://dx.doi.org/10.1109/INFCOM.2012.6195813
dc.identifier.epage	692
dc.identifier.hkuros	202422
dc.identifier.isbn	978-1-4673-0775-8
dc.identifier.issn	0743-166X 2011 SCImago Journal Rankings: 0.047
dc.identifier.scopus	eid_2-s2.0-84861634944
dc.identifier.spage	684
dc.identifier.uri	http://hdl.handle.net/10722/152049
dc.language	eng
dc.publisher	IEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000359
dc.publisher.place	United States
dc.relation.ispartof	IEEE Infocom Proceedings
dc.relation.references	References in Scopus
dc.rights	IEEE 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.rights	Creative Commons: Attribution 3.0 Hong Kong License
dc.subject	Cloud services
dc.subject	Data centers
dc.subject	Demand prediction
dc.subject	Distribution algorithms
dc.subject	Dynamic content
dc.title	Scaling social media applications into geo-distributed clouds
dc.type	Conference_Paper

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Author Affiliations

The University of Hong Kong
University of Calgary
Hong Kong University of Science and Technology