Topology Management Algorithms

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UNIVERSITY OF JYVÄSKYLÄ

Topology Management in Unstructured
P2P Networks Using Neural

Networks


Presentation for IEEE Congress on Evolutionary Computing


27.9.2007


Annemari Auvinen, research student

Department of Mathematical Information Technology

University of Jyväskylä, Finland

http://www.mit.jyu.fi/
cheesefactory


With co
-
authors Teemu Keltanen and Mikko Vapa

UNIVERSITY OF JYVÄSKYLÄ








Topology Management Algorithms


Topology management algorithms affect the
logical topology by making network more
scalable and effective for resource discovery


Use local information the nodes are collecting
about their neighbors


Interest based clustering


Technical

characteristics

of

the

peers

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NeuroTopology


Uses evolutionary neural networks to form
efficient P2P topologies for resource queries


We determine the characteristics that the
neural network should take into account


These characteristics are given to the neural
network as inputs and can be e.g. bandwidth or
information about the previous resource queries


As a result is obtained dynamic P2P network,
where the topology takes shape in interaction
with the resource discovery algorithm

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NeuroTopology



Algorithm is executed in every peer after a
predefined amount of resource queries


Algorithm goes through all neighbor
candidates


To establish a connection mutual agreement
from both nodes is needed

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NeuroTopology


Keep neighbor?

New neighbor?

Neighbor Node

Neighbor’s neighbor

P2P

Node

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Structure of NeuroTopology


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Training Program


Neural network weights define how neural network
behaves so they must be adjusted to right values


This is done using iterative optimization process
based on evolution and Gaussian mutation


Define the

P2P network

conditions

Define the fitness

requirements

for the algorithm

Create candidate

algorithms

randomly

Select the best

ones for next

generation

Breed a new

population

Finally select the

best algorithm for

these conditions

Iterate

thousands

of

generations

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Neural Network Optimization


Evolutionary computing for optimizing the weights


Fitness of the used neural network is defined based
on the amount of traffic in the P2P network.


Algorithm should locate half of the available resources for
each query


Algorithm should use as minimal number of packets and
create as minimum
number of new connections
as possible


Mutation is based on the Gaussian random variation
and uses the weighted mutation parameter to
improve the adaptability of the evolutionary search


Random variation function was introduced by Fogel
and Chellapilla[1]

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Simulation Environment


P2P network with 100 peers


Resources power
-
law distributed


Breadth
-
first search (BFS), highest degree
search (HDS) and random walker (RW) were
used as resource discovery algorithms


The test case was divided to:


Training environment


Generalization environment

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Simulation Environment


In the training set each generation is started
with a grid topology P2P network and follows
the algorithm:

1.
Do 20 times

1.
10 random peers execute resource queries

2.
Execute NeuroTopology algorithm in every peer using
information from resource queries

2.
Execute 10 resource queries in the P2P network

3.
Calculate the fitness for the neural network using
information from step 2


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Simulation Environment


Training of the neural networks was done
using the HDS algorithm and the amount of
generations was 5000


Generalization set was the same as the
training set, except that resource queries
were executed by every peer in the P2P
network



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Fitness in training environment

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Fitness in generalization environment

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Resource query packets and replies in
generalization environment

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Topology packets and changes in
generalization environment

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Failed queries in generalization
environment

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Simulation Results


Tested in grid topology, power
-
law topology and a
random graph topology with 3 resource discovery
algorithms and with and without NeuroTopology


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Convergence


Changing the inefficient grid topology on the
early rounds and limiting the changes when
the efficient topology has been reached

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References

[1] K. Chellapilla and D. Fogel. Evolving neural
networks to play checkers without relying on
expert knowledge. IEEE Trans. on Neural
Networks, 10 (6), pp. 1382
-
1391, 1999.