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Neighbour selection strategies in BitTorrent-like Peer-to-Peer systems

Neighbour selection strategies in BitTorrent-like Peer-to-Peer systems. L.G. Alex Sung, Herman Li March 30, 2005 for CS856 Web Data Management University of Waterloo. Outline. Introduction Problem Statement Proposed Categorization Schemes Experimental Approach Experiment Designs.

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Neighbour selection strategies in BitTorrent-like Peer-to-Peer systems

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  1. Neighbour selection strategies in BitTorrent-like Peer-to-Peer systems L.G. Alex Sung, Herman Li March 30, 2005 for CS856 Web Data Management University of Waterloo

  2. Outline • Introduction • Problem Statement • Proposed Categorization Schemes • Experimental Approach • Experiment Designs

  3. Introduction • BitTorrent Highlights

  4. Problem Statement • Goal: Explore the effect of two neighbour-selection strategies on the efficiency in content distribution for BitTorrent-like Peer-to-Peer systems • Proposed strategies: • Neighbour selection by network capacity • Neighbour selection by locality • Preserve some degree of randomness • What is BT-like? Incentive-built P2P systems with Tit-for-Tat exchange strategy. (central server not required) • Why BT-like? We expect later unstructured P2P systems are BT-like. (eg. eXeem)

  5. Preserving Randomness • Avoid power-law (Zipf) distribution of pieces: • Some pieces may be rare in one domain (capacity or locality), but popular in the other one • Even distribution of pieces increases the sustainability • Randomness preserved by including some fraction of randomly chosen peers of a different domain Linear scales on both axes Logarithmic scales on both axes

  6. Matching by Capacity • Hypothesis: Matching peers according to capacity similarities improves efficiency due to the Tit-for-Tat exchange strategy • When low ability peers are connected to high ability peers: • get pieces when they are being optimistic unchoked • get choked again very quickly as they cannot offer a good exchanging rate

  7. Matching by Locality • Hypothesis: Matching peers by locality: • Benefit from the lower network latency • Better utilization of bandwidth • The topology of the overlay network better matches the underlying network • In the case that the uploading capacity was not previously fully utilized: • maximize the uploading speed by exchanging with peers that are physically closer

  8. Experimental Approach • Run experiments on Planet Lab nodes • Planet Lab nodes experience similar network phenomenon as real BT users • Select a set of Planet Lab nodes that is representative of the user population • Population capacity and locality based on: • A public tracker log for “Beyond Good and Evil” from Dec 03 to Mar 04 [1] NL Netherlands (Europe) 25269 46.90% US United States 10250 19.02% AU Australia 6181 11.47% CA Canada 2730 5.07% 82.46%

  9. Experiment Designs • Experiment 1: Categorization by upload / download rate – sensitivity to randomness • System throughput vs randomness • Experiment 2: Categorization by upload / download rate – sensitivity to number of categories • System throughput vs number of categories • Experiment 3: Categorization by peer locality • System throughput vs randomness • Experiment 4: Combination of improvement schemes • Categorization uses both capacity and locality • System throughput vs randomness

  10. Related work • Anonymous BT with keyword search • eXeem (a commercial product w/ ads) • (IP is not shown directly in the GUI) • Non-random peer set distribution • Based on content availability [2]

  11. References • J.A. Pouwelse, P. Garbacki, D.H.J. Epema, H.J. Sips. The Bittorrent P2P File-sharing System: Measurements and Analysis. 4th International Workshop on Peer-to-Peer Systems (IPTPS'05), Feb 2005 • Simon G. M. Koo, C. S. George Lee, Karthik Kannan: A Genetic-Algorithm-Based Neighbor-Selection Strategy for Hybrid Peer-to-Peer Networks. In Proc. of the International Conference On Computer Communications and Networks (ICCCN 2004), IEEE 2004, pages 469-474, October 2004.

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