Decentralized media streaming infrastructure (DeMSI): An adaptive and high-performance peer-to-peer content delivery network
AuthorWah Yim, AK; Buyya, R
Source TitleJournal of Systems Architecture
PublisherELSEVIER SCIENCE BV
University of Melbourne Author/sBuyya, Rajkumar
AffiliationComputer Science and Software Engineering
Document TypeJournal Article
CitationsWah Yim, A. K. & Buyya, R. (2006). Decentralized media streaming infrastructure (DeMSI): An adaptive and high-performance peer-to-peer content delivery network. Journal of Systems Architecture, 52 (12), pp.737-772. https://doi.org/10.1016/j.sysarc.2006.05.001.
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Hosting an on-demand media content streaming service has been a challenging task mainly because of the outrageously enormous network and server bandwidth required to deliver large amount of content data to users simultaneously. We propose an infrastructure that helps online media content providers offload their server and network resources for media streaming. Using application level resource diversity together with the peer-to-peer resource-sharing model is a feasible approach to decentralize the content storage, server and network bandwidth. Each subscriber is responsible for only a small fraction of such resources. Most importantly, the cost of maintaining the service can also be shared amongst subscribers, especially when the subscriber base is large. As a result, subscribers can be benefit from lower subscription cost. There have been a few solutions out there that focused only on sharing the load of network bandwidth by division of a streaming task to be carried out by multiple sources. However, existing solutions require that the content to be replicated in full and stored in each source, which is impractical for a subscriber as the owner of the storage resource that is of consumer capacity. Our solution focuses on the division of responsibility on both the network bandwidth and content storage such that each subscriber is responsible for only a small portion of the content. We propose a light-weighted candidate peer selection strategy based on avoidance of network congestion and an adaptive re-scheduling algorithm in order to enhance smoothness of the aggregated streaming rate perceived at the consumer side. Experiments show that the performance of our peer-selection strategy out performs the traditional strategy based on end-to-end streaming bandwidth.
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