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

TitleScaling social media applications into geo-distributed clouds
Authors
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
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?
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
Persistent Identifierhttp://hdl.handle.net/10722/152049
ISBN
ISSN
2014 SCImago Journal Rankings: 2.477
References

 

DC FieldValueLanguage
dc.contributor.authorWu, Yen_US
dc.contributor.authorWu, Cen_US
dc.contributor.authorLi, Ben_US
dc.contributor.authorZhang, Len_US
dc.contributor.authorLi, Zen_US
dc.contributor.authorLau, FCMen_US
dc.date.accessioned2012-06-26T06:32:56Z-
dc.date.available2012-06-26T06:32:56Z-
dc.date.issued2012en_US
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. 684-692en_US
dc.identifier.isbn978-1-4673-0775-8-
dc.identifier.issn0743-166Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/152049-
dc.descriptionTS51: Cloud/Grid computing and networks 3-
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.en_US
dc.languageengen_US
dc.publisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000359en_US
dc.relation.ispartofIEEE Infocom Proceedingsen_US
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 cloudsen_US
dc.typeConference_Paperen_US
dc.identifier.emailWu, Y: ywu@cs.hku.hken_US
dc.identifier.emailWu, C: cwu@cs.hku.hken_US
dc.identifier.emailLi, B: bli@cse.ust.hk-
dc.identifier.emailZhang, L: linquan@hku.hk-
dc.identifier.emailLi, Z: zongpeng@ucalgary.ca-
dc.identifier.emailLau, FCM: fcmlau@cs.hku.hk-
dc.identifier.authorityWu, C=rp01397en_US
dc.identifier.authorityLau, FCM=rp00221en_US
dc.description.naturepublished_or_final_versionen_US
dc.identifier.doi10.1109/INFCOM.2012.6195813en_US
dc.identifier.scopuseid_2-s2.0-84861634944en_US
dc.identifier.hkuros202422-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84861634944&selection=ref&src=s&origin=recordpageen_US
dc.identifier.spage684en_US
dc.identifier.epage692en_US
dc.publisher.placeUnited Statesen_US
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.scopusauthoridLau, FCM=7102749723en_US
dc.identifier.scopusauthoridLi, Z=23467418800en_US
dc.identifier.scopusauthoridZhang, L=55233504700en_US
dc.identifier.scopusauthoridLi, B=36071999300en_US
dc.identifier.scopusauthoridWu, C=15836048100en_US
dc.identifier.scopusauthoridWu, Y=47962754000en_US

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