The streaming capacity of sparsely-connected P2P systems with simple, robust and decentralized control
Abstract
Peer-to-Peer (P2P) streaming technologies can take advantage of the upload capacity of clients, and hence can scale to large content distribution networks with lower cost. It is highly desirable that most of the control decisions be decentralized, with each peer only interacting with a small number of other peers/neighbors. However, this requirement has made the analytical study of the performance of P2P systems particularly challenging. In this thesis, we rigorously study the achievable streaming capacity of large-scale P2P streaming systems, either live streaming or video-on-demand, with sparse connectivity among peers, and investigate simple and decentralized P2P control strategies that can provably achieve close-to-optimal streaming capacity. We show that even with a random peer-selection algorithm and uniform rate allocation, as long as each peer maintains Θ (log N) downstream neighbors, where N is the total number of peers in the system, the system can asymptotically achieve a streaming rate that is close to the optimal streaming rate of a complete network. Further, the tracker does not need to obtain detailed knowledge of which chunks each peer caches, and hence incurs low overhead. We then study multiple streaming channels where peers watching one channel may help in another channel with insufficient upload bandwidth. We propose a simple random cache-placement strategy, and show that a close-to-optimal streaming capacity region for all channels can be attained with high probability, again with only Θ (log N) per-peer neighbors. A distinct advantage of the cache placement strategy is its robustness to imprecise estimates of system parameters such as video popularity and upload bandwidth distribution. Simulation results are provided to verify our analysis.
Degree
Ph.D.
Advisors
Lin, Purdue University.
Subject Area
Electrical engineering|Technical Communication
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