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. Experimental Study on Neighbor Selection Policy for Phoenix Network Coordinate System

. Experimental Study on Neighbor Selection Policy for Phoenix Network Coordinate System. Gang Wang , Shining Wu, Guodong Wang, Beixing Deng, Xing Li Tsinghua University. Outline. Introduction Related work System design Performance evaluation Conclusion. Introduction.

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. Experimental Study on Neighbor Selection Policy for Phoenix Network Coordinate System

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  1. .Experimental Study on Neighbor Selection Policy for Phoenix Network Coordinate System Gang Wang, Shining Wu, Guodong Wang, Beixing Deng, Xing Li Tsinghua University Tsinghua Univ.

  2. Outline • Introduction • Related work • System design • Performance evaluation • Conclusion Tsinghua Univ.

  3. Introduction • Network Coordinate System (NCS) • Distance(Latency) information is very important for large scale network applications: P2P, Overlay Multicast, Overlay routing… • NCS maps the network into a mathematical space Distance Estimation Nearest neighbor awareness others… Network Mathematical space Tsinghua Univ.

  4. Introduction • Network Coordinate System (NCS) • Network Coordinate System predicts End-to-End Links by measurement: Scalability • High accuracy and scalability • Low overhead (Linear) N Measured Distance N Estimated Distance Tsinghua Univ.

  5. Introduction • NC System related Applications • Google CDN (GNP NCS for sever selection) • Vuze BitTorrent (NC for neighbor selection) • SBON(NC for Data query) • … Tsinghua Univ.

  6. Introduction • Problem • The recently proposed Phoenix NCS is a promising solution : • Avoids the Triangle Inequality Violation(TIV) problem • High accuracy and convergence rate • Robustness over measurement anomalies • Phoenix NCS suffers disadvantage in certain applications such as Overlay Multicast • The neighbor selection policy for Phoenix is a possible solution to this problem Tsinghua Univ.

  7. Related Work • Phoenix Network Coordinate System • Each node will be associated to a Network Coordinate (NC) Is random neighbor selection is the best? • For each new node: m • select any M existing hosts randomly • m measures its RTTs to these M hosts as well as retrieves the NCs of these M hosts. • NC can be calculated and updated periodically. M m Tsinghua Univ.

  8. System Design Random Policy Closest Policy Hybrid Policy • Random Policy: Randomly select M reference neighbors • Closest Policy: Choose M closest nodes as reference • Hybrid Policy: Mc Closest Nodes and Mr randomly selected nodes as reference Tsinghua Univ.

  9. System Design • Hybrid intuition • Distant reference nodes: to locate its position • Nearby reference nodes: to adjust it NC to reach high accuracy Closest nodes Accurate Location Target node Distant nodes Tsinghua Univ.

  10. Performance Evaluation • Experimental Set up • Data set and Metrics • Prediction accuracy • Application on Overlay Multicast Tsinghua Univ.

  11. Performance Evaluation • Experimental Set up • All of these three systems use 10-dimensional coordinates. • Each node has M reference nodes (M=32) • All of these systems have10 runs on each data set and an average result is reported • For Hybrid: Mc = 6 (The number of closest reference nodes) Mr = M – Mc =26 Tsinghua Univ.

  12. Performance Evaluation • Datasets and Metrics • The PlanetLab data set: 226 hosts all over the earth • The King data set:1740 Internet DNS servers. • Distance prediction Relative Error(RE) • Nearest Neighbor Loss (NNL) the difference between the estimated nearest host by NCS and the true one Tsinghua Univ.

  13. Performance Evaluation • Prediction accuracy • Mean RE • Smaller RE indicates higher prediction accuracy • Hybrid achieves lower RE than Random and Closest over both data set Tsinghua Univ.

  14. Performance Evaluation • Prediction accuracy • NNL • Smaller NNL indicates better ability to select nearest host • Hybrid achieves lower NNL than Random and Closest over both data set Tsinghua Univ.

  15. Performance Evaluation • Application on Overlay Multicast • What to do • Multicast Tree constructed according the predicted distance by NCS • The quality of the multicast tree is evaluated by tree cost (the sum of latencies of all tree links) • The tree cost reflects the distance prediction accuracy of NCS • Two kinds of multicast tree: ESM & MST Tsinghua Univ.

  16. Performance Evaluation • Application on Overlay Multicast • Everage tree cost on PlanetLab and King ESM-PlanetLab ESM-King Tsinghua Univ.

  17. Performance Evaluation • Application on Overlay Multicast • Everage tree cost on PlanetLab and King MST-PlanetLab MST-King • Reduce the average tree cost by at least 20% Tsinghua Univ.

  18. Performance Evaluation • Application on Overlay Multicast • tree cost change as the tree size increases over King ESM-King MST-King • Lower growth rate & Lower tree cost Tsinghua Univ.

  19. Conclusion • Phoenix with Hybrid neighbor selection policy achieves • Lower distance relative prediction error • a better accuracy in selecting nearest host • A better performance in the application of Overlay Multicast Tsinghua Univ.

  20. Any Questions? Thank you Tsinghua Univ.

  21. More NC Research: Simulator: http://www.netglyph.org/~wanggang/Phoenix_NCS_sim.zip Gang Wang’s Homepage: http://www.net-glyph.org/~wanggang/ More about NC research in Tsinghua: http://www.netglyph.org/~netcoord/ Tsinghua Univ.

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