Issue No. 02 - Feb. (2013 vol. 62)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TC.2011.222
Fangming Liu , Huazhong University of Science and Technology, Wuhan
Bo Li , Hong Kong University of Science and Technology, Hong Kong
Baochun Li , University of Toronto, Toronto
Hai Jin , Huazhong University of Science and Technology, Wuhan
Nowadays, there has been significant deployment of peer-assisted on-demand streaming services over the Internet. Two of the most unique and salient features in a peer-assisted on-demand streaming system are the differentiation in the demand (or request) and the prefetching capability with caching. In this paper, we develop a theoretical framework based on queuing models, in order to 1) justify the superiority of service prioritization based on a taxonomy of requests, and 2) understand the fundamental principles behind optimal prefetching and caching designs in peer-assisted on-demand streaming systems. The focus is to instruct how limited uploading bandwidth resources and peer caching capacities can be utilized most efficiently to achieve better system performance. To achieve these objectives, we first use priority queuing analysis to prove how service quality and user experience can be statistically guaranteed, by prioritizing requests in the order of significance, including urgent playback (e.g., random seeks or initial startup), normal playback, and prefetching. We then proceed to construct a fine-grained stochastic supply-demand model to investigate peer caching and prefetching as a global optimization problem. This not only provides insights in understanding the fundamental characterization of demand, but also offers guidelines toward optimal prefetching and caching strategies in peer-assisted on-demand streaming systems.
Prefetching, Streaming media, Bandwidth, Servers, Queueing analysis, Peer to peer computing, Analytical models, performance evaluation, On-demand video streaming, peer-to-peer, queuing model
B. Li, B. Li, F. Liu and H. Jin, "Peer-Assisted On-Demand Streaming: Characterizing Demands and Optimizing Supplies," in IEEE Transactions on Computers, vol. 62, no. , pp. 351-361, 2013.