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Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/6607
Title: Optimizing the throughput of data-driven peer-to-peer streaming
Authors: Zhang, Meng
Xiong, Yongqiang
Zhang, Qian
Sun, Lifeng
Yang, Shiqiang
Keywords: Peer-to-peer
Data driven
Block scheduling
Min cost flow
Throughput
Delivery ratio
Issue Date: Jan-2009
Citation: IEEE transactions on parallel and distributed systems, vol. 20, no. 1, January 2009, p. 97-110
Abstract: During recent years, the Internet has witnessed a rapid growth in deployment of data-driven (or swarming based) peer-topeer (P2P) media streaming. In these applications, each node independently selects some other nodes as its neighbors (i.e., gossipstyle overlay construction) and exchanges streaming data with the neighbors (i.e., data scheduling). To improve the performance of such protocol, many existing works focus on the gossip-style overlay construction issue. However, few of them concentrate on optimizing the streaming data scheduling to maximize the throughput of a constructed overlay. In this paper, we analytically study the scheduling problem in data-driven streaming system and model it as a classical min-cost network flow problem. We then propose both the global optimal scheduling scheme and distributed heuristic algorithm to optimize the system throughput. Furthermore, we introduce layered video coding into data-driven protocol and extend our algorithm to deal with the end-host heterogeneity. The results of simulation with the real-world traces indicate that our distributed algorithm significantly outperforms conventional ad hoc scheduling strategies especially in stringent buffer and bandwidth constraints.
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URI: http://hdl.handle.net/1783.1/6607
Appears in Collections:CSE Journal/Magazine Articles

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