1. Gang Wang, Shining Wu, Guodong Wang, Beixing Deng, Xing Li Tsinghua University Tsinghua Univ. Oct.2009 1. Experimental Study on Neighbor Selection Policy for Phoenix Network Coordinate System

2. Outline Oct.2009 Tsinghua Univ. 2 Introduction Related work System design Performance evaluation Conclusion

3. Introduction Oct.2009 Tsinghua Univ. 3 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 Network Mathematical space Distance Estimation Nearest neighbor awareness others…

4. Introduction Oct.2009 Tsinghua Univ. 4 Network Coordinate System (NCS)  Network Coordinate System predicts End-to-End Links by measurement: Scalability  High accuracy and scalability  Low overhead (Linear) Estimated Distance Measured Distance N N

5. Introduction Oct.2009 Tsinghua Univ. 5 NC System related Applications Google CDN (GNP NCS for sever selection) Vuze BitTorrent (NC for neighbor selection) SBON(NC for Data query) …

6. Introduction Oct.2009 Tsinghua Univ. 6 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

7. Related Work Oct.2009 Tsinghua Univ. 7 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

8. System Design Oct.2009 Tsinghua Univ. 8 Random PolicyClosest PolicyHybrid 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

9. System Design Oct.2009 Tsinghua Univ. 9 Hybrid intuition  Distant reference nodes: to locate its position  Nearby reference nodes: to adjust it NC to reach high accuracy Closest nodes Target node Distant nodes Accurate Location

10. Performance Evaluation Oct.2009 Tsinghua Univ. 10 Experimental Set up Data set and Metrics Prediction accuracy Application on Overlay Multicast

11. Performance Evaluation Oct.2009 Tsinghua Univ. 11 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

12. Performance Evaluation Oct.2009 Tsinghua Univ. 12 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

13. Performance Evaluation Oct.2009 Tsinghua Univ. 13 Prediction accuracy  Mean RE  Smaller RE indicates higher prediction accuracy  Hybrid achieves lower RE than Random and Closest over both data set Data Set NCS PlanetLabKing Random0.23630.2416 Hybrid0.13770.1567 Closest1.65480.8791

14. Performance Evaluation Oct.2009 Tsinghua Univ. 14 Prediction accuracy  NNL  Smaller NNL indicates better ability to select nearest host  Hybrid achieves lower NNL than Random and Closest over both data set Data Set NCS PlanetLabKing Random21.08520.8871 Hybrid13.499514.8103 Closest112.394153.5009

15. Performance Evaluation Oct.2009 Tsinghua Univ. 15 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

16. Performance Evaluation Oct.2009 Tsinghua Univ. 16 Application on Overlay Multicast Everage tree cost on PlanetLab and King ESM-PlanetLab ESM-King

17. Performance Evaluation Oct.2009 Tsinghua Univ. 17 Application on Overlay Multicast Everage tree cost on PlanetLab and King MST-PlanetLab MST-King Reduce the average tree cost by at least 20%

18. Performance Evaluation Oct.2009 Tsinghua Univ. 18 Application on Overlay Multicast tree cost change as the tree size increases over King ESM-KingMST-King Lower growth rate & Lower tree cost

19. Conclusion Oct.2009 Tsinghua Univ. 19 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

20. THANK YOU Tsinghua Univ. Oct.2009 20 Any Questions?

21. More NC Research: Tsinghua Univ. Oct.2009 21 Simulator: http://www.netglyph.org/~wanggang/Phoenix_NCS_sim.ziphttp://www.netglyph.org/~wanggang/Phoenix_NCS_sim.zip Gang Wang’s Homepage: http://www.net-glyph.org/~wanggang/http://www.net-glyph.org/~wanggang/ More about NC research in Tsinghua: http://www.netglyph.org/~netcoord/http://www.netglyph.org/~netcoord/