Conference Paper: CloudMedia: When cloud on demand meets video on demand

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Publisher Website: 10.1109/ICDCS.2011.50
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Title	CloudMedia: When cloud on demand meets video on demand
Authors	Wu, Y1 Wu, C1 Li, B2 Qiu, X1 Lau, FCM1
Keywords	Algorithm design Client server Computation-intensive task Computing paradigm Dynamic demand
Issue Date	2011
Publisher	IEEE, Computer Society.
Citation	The 31st International Conference on Distributed Computing Systems (ICDCS 2011), Minneapolis, MN., 20-24 June 2011. In Proceedings of 31st ICDCS, 2011, p. 268-277 [How to Cite?] DOI: http://dx.doi.org/10.1109/ICDCS.2011.50
Abstract	Internet-based cloud computing is a new computing paradigm aiming to provide agile and scalable resource access in a utility-like fashion. Other than being an ideal platform for computation-intensive tasks, clouds are believed to be also suitable to support large-scale applications with periods of flash crowds by providing elastic amounts of bandwidth and other resources on the fly. The fundamental question is how to configure the cloud utility to meet the highly dynamic demands of such applications at a modest cost. In this paper, we address this practical issue with solid theoretical analysis and efficient algorithm design using Video on Demand (VoD) as the example application. Having intensive bandwidth and storage demands in real time, VoD applications are purportedly ideal candidates to be supported on a cloud platform, where the on-demand resource supply of the cloud meets the dynamic demands of the VoD applications. We introduce a queueing network based model to characterize the viewing behaviors of users in a multichannel VoD application, and derive the server capacities needed to support smooth playback in the channels for two popular streaming models: client-server and P2P. We then propose a dynamic cloud resource provisioning algorithm which, using the derived capacities and instantaneous network statistics as inputs, can effectively support VoD streaming with low cloud utilization cost. Our analysis and algorithm design are verified and extensively evaluated using large-scale experiments under dynamic realistic settings on a home-built cloud platform. © 2011 IEEE.
ISSN	1063-6927 2011 SCImago Journal Rankings: 0.032
DOI	http://dx.doi.org/10.1109/ICDCS.2011.50
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	Qiu, X
dc.contributor.author	Lau, FCM
dc.date.accessioned	2011-07-27T01:39:55Z
dc.date.available	2011-07-27T01:39:55Z
dc.date.issued	2011
dc.description.abstract	Internet-based cloud computing is a new computing paradigm aiming to provide agile and scalable resource access in a utility-like fashion. Other than being an ideal platform for computation-intensive tasks, clouds are believed to be also suitable to support large-scale applications with periods of flash crowds by providing elastic amounts of bandwidth and other resources on the fly. The fundamental question is how to configure the cloud utility to meet the highly dynamic demands of such applications at a modest cost. In this paper, we address this practical issue with solid theoretical analysis and efficient algorithm design using Video on Demand (VoD) as the example application. Having intensive bandwidth and storage demands in real time, VoD applications are purportedly ideal candidates to be supported on a cloud platform, where the on-demand resource supply of the cloud meets the dynamic demands of the VoD applications. We introduce a queueing network based model to characterize the viewing behaviors of users in a multichannel VoD application, and derive the server capacities needed to support smooth playback in the channels for two popular streaming models: client-server and P2P. We then propose a dynamic cloud resource provisioning algorithm which, using the derived capacities and instantaneous network statistics as inputs, can effectively support VoD streaming with low cloud utilization cost. Our analysis and algorithm design are verified and extensively evaluated using large-scale experiments under dynamic realistic settings on a home-built cloud platform. © 2011 IEEE.
dc.description.nature	published_or_final_version
dc.description.other	The 31st International Conference on Distributed Computing Systems (ICDCS 2011), Minneapolis, MN., 20-24 June 2011. In Proceedings of 31st ICDCS, 2011, p. 268-277
dc.identifier.citation	The 31st International Conference on Distributed Computing Systems (ICDCS 2011), Minneapolis, MN., 20-24 June 2011. In Proceedings of 31st ICDCS, 2011, p. 268-277 [How to Cite?] DOI: http://dx.doi.org/10.1109/ICDCS.2011.50
dc.identifier.doi	http://dx.doi.org/10.1109/ICDCS.2011.50
dc.identifier.epage	277
dc.identifier.hkuros	187762
dc.identifier.isi	WOS:000295121900026
dc.identifier.issn	1063-6927 2011 SCImago Journal Rankings: 0.032
dc.identifier.scopus	eid_2-s2.0-80051870839
dc.identifier.spage	268
dc.identifier.uri	http://hdl.handle.net/10722/135699
dc.language	eng
dc.publisher	IEEE, Computer Society.
dc.relation.ispartof	International Conference on Distributed Computing Systems Proceedings, ICDCS 2011
dc.relation.references	References in Scopus
dc.rights	Creative Commons: Attribution 3.0 Hong Kong License
dc.rights	International Conference on Distributed Computing Systems Proceedings. Copyright © IEEE, Computer Society.
dc.rights	©2011 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.subject	Algorithm design
dc.subject	Client server
dc.subject	Computation-intensive task
dc.subject	Computing paradigm
dc.subject	Dynamic demand
dc.title	CloudMedia: When cloud on demand meets video on demand
dc.type	Conference_Paper

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Author Affiliations

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