# Network Coding in Peer-to-Peer Networks Presented by Chu Chun Ngai 1008624233.

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Network Coding in Peer-to-Peer Networks Presented by Chu Chun Ngai 1008624233

Content P2P Content Distribution P2P Multimedia Streaming

Introduction Network coding brings new insights Application on P2P networks Random network coding needed Complexity in theoretical and computational considerations

P2P Content Distribution A server and multiple peers Peers connect to their neighbors The file is divided into k equal-sized blocks Swarming techniques: exchange blocks with neighbors until all k blocks are collected E.g. BitTorrent (BT)

P2P Content Distribution Question: 1. Which blocks to be downloaded 2. Shortest time needed Normal solution: 1. Local rarest first 2. Uncontrollable due to extremely rare blocks

P2P Content Distribution With network coding Each block is treated as a vector over finite field Users upload linear combinations of blocks Then blindly download coded blocks until all blocks can be recovered by algebraic operation E.g. Microsoft Avalanche

P2P Content Distribution S 1 2 3 4 5 b1b1 b2b2 b1b1 b2b2 c 11 b 1 +c 12 b 2 c 21 b 1 +c 22 b 2 b1b1 b2b2 Example

P2P Content Distribution Theory Overlay network: a directed graph G=(V, E) Node s: server Other nodes are peers Not good for dynamic network

P2P Content Distribution Trellis graph technique Consider time evolution A new directed acyclic graph G* Transmission edges and Accumulation edges Apply flow analysis on G*

P2P Content Distribution Example (From Chapter 3 of “Network Coding - Fundamentals and Applications”, edited by M. Medard and A. Sprintson, Acadamic Press, 2012. )

P2P Content Distribution Given a set of peers U t Minimum time t opt (U)=inf{t : maxflow(U t ) >= k} By result in random gossiping [1] Time for all n peers to download all k blocks Upper bounded by ck + O( sqrt(k log k) log n ) with high probability [1] S. Deb, M. Medard, and C. Choute. “Algebraic gossip: A network coding approach to optimal multiple rumor mongering”, IEEE Trans. Inf. Theory, vol. 52, no. 6, pp.2486 – 2507, June 2006.

P2P Content Distribution Limitations Heavy computational expense Break the file into generations first Difficulty in constructing protocols which include method of creating generations Few real-life applications ongoing

P2P Multimedia Streaming Streaming service demands increasing Peers contribute upload bandwidth to others Performance metrics Playback quality Initial buffering delay Server bandwidth costs Network scales

P2P Multimedia Streaming Similar to content distribution: random gossiping Differently a sliding window of blocks over time is transmitted Still have to decide which peer to download

P2P Multimedia Streaming Streaming strategies Tree based push Mesh based pull Each peer maintains a playback buffer that consists of data blocks to be played in the immediate future Every peer periodically exchanges block availability information of buffer maps with its neighbors Data blocks are pulled from appropriate neighbors

P2P Multimedia Streaming Pulling may cause block overhead Playbacks would be skipped causing low streaming quality Apply random network coding!

P2P Multimedia Streaming Random Push Peer generate a linear combination of blocks and transmit to following peers Decode after receiving enough blocks

P2P Multimedia Streaming Buffer maps indicate segment availability information – suitable for large segments of blocks Synchronized playback Only retrieves segments that are D seconds after the current playback point, where D corresponds to the initial buffering delay Playback buffers of peers overlap as much as possible

P2P Multimedia Streaming Notations U i Upload capacity of a class-i peer (in blocks per second) U p Average upload capacity of participating peers U s Server upload capacity (in blocks per second) R Streaming rate (in blocks per second) D Initial buffering delay (in seconds) N Scale of a flash crowd (the number of participating peers in the system) k Number of data blocks in each segment d Server strength =U s / NU p

P2P Multimedia Streaming Theorem [2]. The maximum streaming rate R max is given by: R max = U p + U s / N, where N is the number of participating peers in the system. [2] R. Kumar, Y. Liu, and K. W. Ross. “Stochastic Fluid Theory for P2P Streaming”, Systems. Proc. of IEEE INFOCOM, May 6–12, 2007.

P2P Multimedia Streaming Theorem. Let D s be the random variable denoting the buffering delay for a segment under the system model. Then for any given push strategy: E[Ds] >= kN / (U s + NU p ) where N is the number of participating peers in the system, and k is the number of data blocks in each segment.

P2P Multimedia Streaming Braking redundancy A downstream peer may receive additional redundant blocks after a segment is complete Minimize this A solution: early braking

Conclusion Efficiently improve aspects of P2P network systems like time and bandwidth Difficulty in designing algorithms makes real-world applications unapplied Still in prototype, have a far way to be a popular product

Reference B. Li and D. Niu, “Random Network Coding in Peer-to-Peer Networks: From Theory to Practice”, Proceeding of the IEEE, Special Issue on Network Coding, edited by B. Li and Y. Wu, Mar, 2011. C. Feng and B. Li, “Network Coding for Content Distribution and Multimedia Streaming in Peer-to-Peer Networks”, Ch.3, Network Coding - Fundamentals and Applications, edited by M. Medard and A. Sprintson, Acadamic Press, 2012.

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