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

This study presents an experimental evaluation of different neighbor selection policies for the Phoenix Network Coordinate System, examining their performance and accuracy in applications such as overlay multicast. The results show that the hybrid policy achieves lower distance prediction errors and better accuracy in selecting nearest hosts, resulting in improved performance in overlay multicast applications.

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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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