Date of Award
M.S. in Engineering Science
Computer and Information Science
John N. Daigle
BitTorrent is the most popular Peer-to-Peer (P2P) file sharing system widely used for distributing large files over the Internet. It has attracted extensive attentions from both network operators and researchers for investigating its deployment and performance. For example, recent studies have shown that under steady state, its rarest first scheme with the tit-for-tat mechanism can work very effectively and make BitTorrent near optimal for the generic file downloading process. However, in practice, the highly dynamic network environment, especially the notorious user churns prevalently existing in most peer-to-peer systems, can severely degrade the downloading performance. In this thesis, we first study on the limitations of BitTorrent under dynamic network environments, focusing on two scenarios where with our preliminary modeling and analysis, we clearly identify how network dynamics and peer churns can significantly degrade the performance. With these findings, we further propose a novel protocol named RaptorQP2P, which is based on RaptorQ coding, to overcome the limitations of current BitTorrent design and maximize the performance of P2P file distribution. The new protocol features two levels of RaptorQ encoding. At the top layer, the entire file is RaptorQ encoded to yield a collection of source blocks and repair blocks, and then each source and repair block is RaptorQ encoded independently to yield a collection of source symbols and repair symbols for the block. The symbols are independently transferred among the peers and when a sufficient number of distinct symbols for a particular block have been received, whether source or repair, the block can be reconstructed. The file can be reconstructed using a sufficient arbitrary number of distinct blocks. Our results show that RaptorQP2P can well handle the network dynamics as well as peer churns and significantly shorten the downloading completion time by up to 41.4% with excellent scalability on both file size and user population.
Su, Zeyang, "Raptorqp2P: Maximize The Performance Of P2P File Distribution With Raptorq Coding" (2014). Electronic Theses and Dissertations. 450.
Emphasis: Computer Science