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. 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
.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.

outline
Outline
  • Introduction
  • Related work
  • System design
  • Performance evaluation
  • Conclusion

Tsinghua Univ.

introduction
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.

introduction1
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.

introduction2
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.

introduction3
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.

related work
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.

system design
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.

system design1
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.

performance evaluation
Performance Evaluation
  • Experimental Set up
  • Data set and Metrics
  • Prediction accuracy
  • Application on Overlay Multicast

Tsinghua Univ.

performance evaluation1
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.

performance evaluation2
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.

performance evaluation3
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.

performance evaluation4
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.

performance evaluation5
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.

performance evaluation6
Performance Evaluation
  • Application on Overlay Multicast
  • Everage tree cost on PlanetLab and King

ESM-PlanetLab

ESM-King

Tsinghua Univ.

performance evaluation7
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.

performance evaluation8
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.

conclusion
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.

any questions
Any Questions?

Thank you

Tsinghua Univ.

more nc research
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.