Decentralized media streaming infrastructure (DeMSI): An adaptive and high-performance peer-to-peer content delivery network

  • Authors:
  • Alan Kin Wah Yim;Rajkumar Buyya

  • Affiliations:
  • Grid Computing and Distributed Systems (GRIDS) Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Carlton, Melbourne VIC 3053, Australia;Grid Computing and Distributed Systems (GRIDS) Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Carlton, Melbourne VIC 3053, Australia

  • Venue:
  • Journal of Systems Architecture: the EUROMICRO Journal
  • Year:
  • 2006

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Abstract

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.