Gang Wang, Shining Wu, Guodong Wang, Beixing Deng, Xing Li

Tsinghua University

Tsinghua Univ. Oct.2009

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Experimental Study on Neighbor Selection Policy for Phoenix Network Coordinate System

Outline

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IntroductionRelated workSystem designPerformance evaluationConclusion

Introduction

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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 Distance Estimation

Nearest neighbor awareness

Nearest neighbor awareness others…others…

Introduction

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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

( )O N

2( )O N

Introduction

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NC System related Applications

Google CDN (GNP NCS for sever selection)

Vuze BitTorrent (NC for neighbor selection)

SBON(NC for Data query)

…

Introduction

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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

Related Work

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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.

mM

System Design

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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

System Design

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Hybrid intuition Distant reference nodes: to locate its position Nearby reference nodes: to adjust it NC to reach high

accuracy

Closest nodes Target nodeDistant nodes

Accurate Location

Performance Evaluation

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Experimental Set upData set and MetricsPrediction accuracy Application on Overlay Multicast

Performance Evaluation

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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

Performance Evaluation

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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

),(

),(),(),(

jiD

jiDjiDjiRE

E

Performance Evaluation

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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

PlanetLab King

Random 0.2363 0.2416Hybrid 0.1377 0.1567Closest 1.6548 0.8791

Performance Evaluation

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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

PlanetLab King

Random 21.085 20.8871Hybrid 13.4995 14.8103Closest 112.3941 53.5009

Performance Evaluation

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Application on Overlay MulticastWhat 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

Performance Evaluation

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Application on Overlay MulticastEverage tree cost on PlanetLab and King

ESM-PlanetLab ESM-King

Performance Evaluation

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Application on Overlay MulticastEverage tree cost on PlanetLab and King

MST-PlanetLab MST-King

Reduce the average tree cost by at least 20%

Performance Evaluation

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Application on Overlay Multicasttree cost change as the tree size increases over King

ESM-King MST-King

• Lower growth rate & Lower tree cost

Conclusion

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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

THANK YOU

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Any Questions?

More NC Research:

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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/