1.
Experimental Study on Neighbor
Selection Policy for Phoenix Network
Coordinate System
Gang Wang, Shining Wu, Guodong Wang,
Beixing Deng, Xing Li
Ts i n g h u a U n i v e r s i t y
Tsinghua Univ.
Oct.2009
Outline
2
 Introduction
 Related work
 System design
 Performance evaluation
 Conclusion
Tsinghua Univ.
Oct.2009
Introduction
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
Distance Estimation
Nearest neighbor
awareness
others…
Network
Tsinghua Univ.
Mathematical space
Oct.2009
Introduction
4
 Network Coordinate System (NCS)
2
O
(
N
) End-to-End Links
 Network Coordinate System predicts
by O( N ) measurement: Scalability


High accuracy and scalability
Low overhead (Linear)
N
Measured Distance
N
Tsinghua Univ.
Estimated Distance
Oct.2009
Introduction
5
 NC System related Applications

Google CDN (GNP NCS for sever selection)

Vuze BitTorrent (NC for neighbor selection)

SBON(NC for Data query)

…
Tsinghua Univ.
Oct.2009
Introduction
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
Tsinghua Univ.
Oct.2009
Related Work
7
 Phoenix Network Coordinate System

Each node will be associated to a Network Coordinate (NC)
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.
M
m
 NC can be calculated and
updated periodically.
Is random neighbor selection is the best?
Tsinghua Univ.
Oct.2009
System Design
8
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.
Oct.2009
System Design
9
 Hybrid intuition
 Distant reference nodes: to locate its position
 Nearby reference nodes: to adjust it NC to reach high accuracy
Accurate Location
Tsinghua Univ.
Closest nodes
Target node
Distant nodes
Oct.2009
Performance Evaluation
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 Experimental Set up
 Data set and Metrics
 Prediction accuracy
 Application on Overlay Multicast
Tsinghua Univ.
Oct.2009
Performance Evaluation
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
Tsinghua Univ.
Oct.2009
Performance Evaluation
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)
RE (i, j ) 

D E (i, j )  D(i, j )
D(i, j )
Nearest Neighbor Loss (NNL)
the difference between the estimated nearest host by NCS and the
true one
Tsinghua Univ.
Oct.2009
Performance Evaluation
13
 Prediction accuracy

Mean RE
Data Set
PlanetLab
King
Random
0.2363
0.2416
Hybrid
0.1377
0.1567
Closest
1.6548
0.8791
NCS


Smaller RE indicates higher prediction accuracy
Hybrid achieves lower RE than Random and Closest over both data
set
Tsinghua Univ.
Oct.2009
Performance Evaluation
14
 Prediction accuracy

NNL
Data Set
PlanetLab
King
21.085
13.4995
112.3941
20.8871
14.8103
53.5009
NCS
Random
Hybrid
Closest


Smaller NNL indicates better ability to select nearest host
Hybrid achieves lower NNL than Random and Closest over both data
set
Tsinghua Univ.
Oct.2009
Performance Evaluation
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
Tsinghua Univ.
Oct.2009
Performance Evaluation
16
 Application on Overlay Multicast
 Everage tree cost on PlanetLab and King
ESM-PlanetLab
Tsinghua Univ.
ESM-King
Oct.2009
Performance Evaluation
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%
Tsinghua Univ.
Oct.2009
Performance Evaluation
18
 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.
Oct.2009
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
Tsinghua Univ.
Oct.2009
20
THANK YOU
Any Questions?
Tsinghua Univ.
Oct.2009
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/
Tsinghua Univ.
Oct.2009