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Proceedings Paper

A distributed multichannel demand-adaptive P2P VoD system with optimized caching and neighbor-selection
Author(s): Hao Zhang; Minghua Chen; Abhay Parekh; Kannan Ramchandran
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Paper Abstract

We design a distributed multi-channel P2P Video-on-Demand (VoD) system using "plug-and-play" helpers. Helpers are heterogenous "micro-servers" with limited storage, bandwidth and number of users they can serve simultaneously. Our proposed system has the following salient features: (1) it jointly optimizes over helper-user connection topology, video storage distribution and transmission bandwidth allocation; (2) it minimizes server load, and is adaptable to varying supply and demand patterns across multiple video channels irrespective of video popularity; and (3) it is fully distributed and requires little or no maintenance overhead. The combinatorial nature of the problem and the system demand for distributed algorithms makes the problem uniquely challenging. By utilizing Lagrangian decomposition and Markov chain approximation based arguments, we address this challenge by designing two distributed algorithms running in tandem: a primal-dual storage and bandwidth allocation algorithm and a "soft-worst-neighbor-choking" topology-building algorithm. Our scheme provably converges to a near-optimal solution, and is easy to implement in practice. Packet-level simulation results show that the proposed scheme achieves minimum sever load under highly heterogeneous combinations of supply and demand patterns, and is robust to system dynamics of user/helper churn, user/helper asynchrony, and random delays in the network.

Paper Details

Date Published: 24 September 2011
PDF: 19 pages
Proc. SPIE 8135, Applications of Digital Image Processing XXXIV, 81350X (24 September 2011); doi: 10.1117/12.896617
Show Author Affiliations
Hao Zhang, Univ. of California, Berkeley (United States)
Minghua Chen, The Chinese Univ. of Hong Kong (Hong Kong, China)
Abhay Parekh, Univ. of California, Berkeley (United States)
Kannan Ramchandran, Univ. of California, Berkeley (United States)


Published in SPIE Proceedings Vol. 8135:
Applications of Digital Image Processing XXXIV
Andrew G. Tescher, Editor(s)

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