Challenges, design and analysis of a large-scale p2p-vod system
By Yan Huang, Tom Zhengjia Fu, D. Chiu, John C.S. Lui, Cheng Huang. Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2008
650 citations recorded by the source corpus.
P2P file downloading and streaming have already become very popular Internet applications. These systems dramatically reduce the server loading, and provide a platform for scalable content distribution, as long as there is interest for the content. P2P-based video-on-demand (P2P-VoD) is a new challenge for the P2P technology. Unlike streaming live content, P2P-VoD has less synchrony in the users sharing video content, therefore it is much more difficult to alleviate the server loading and at the same time maintaining the streaming performance. To compensate, a small storage is contributed by every peer, and new mechanisms for coordinating content replication, content discovery, and peer scheduling are carefully designed. In this paper, we describe and discuss the challenges and the architectural design issues of a large-scale P2P-VoD system based on the experiences of a real system deployed by PPLive. The system is also designed and instrumented with monitoring capability to measure both system and component specific performance metrics (for design improvements) as well as user satisfaction. After analyzing a large amount of collected data, we present a number of results on user behavior, various system performance metrics, including user satisfaction, and discuss what we observe based on the system design. The study of a real life system provides valuable insights for the future development of P2P-VoD technology.
Subfields: Computer Networks and Communications, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/28ea425e48eb7e38df55b74d9c2c816ec47bdc18
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Citations in the corpus
- Understanding the impact of video quality on user engagement (2011, background): “Content popularity: There is an extensive literature on modeling content popularity and its subsequent implications for caching (e.g., [15,18,22,24,26]).”