UNIVERSITY OF JYVÄSKYLÄ Resource Discovery in P2P Networks Using Evolutionary Neural Networks Presentation for International Conference on Advances in

UNIVERSITY OF JYVÄSKYLÄ

Resource Discovery in P2P Networks Using Evolutionary Neural Networks Presentation for International Conference on Advances in Intelligent Systems – Theory and Applications (AISTA 2004)

15.11.2004

Mikko Vapa, researcher studentAgora Center

http://tisu.it.jyu.fi/cheesefactory

With co-authors Niko Kotilainen, Annemari Auvinen, Heikki Kainulainen and Jarkko Vuori

2004


Peer-to-Peer Networks

• Peer-to-Peer networks (P2P) are formed by Transmission Control Protocol (TCP) connections between workstations

• Workstations denoted as nodes can share their resources for example files ( ) or computing power

Node 1

Node 2

Node 3

Node 4

TCP

TCP

TCP

TCP

TCP

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Resource Discovery Problem

• In peer-to-peer resource discovery problem any node in the network can query resources from other nodes

Node1: Where is ?

Node 1

Node 2

Node 3

Node 4

2004


A Simple Solution for the Problem

• Gnutella P2P network for example uses Breadth-First Search (BFS) flooding algorithm which sends query to all neighbors

• Problems: all resources in the network can be found, but network gets congested and there are lots of useless packets

Node 1: Where is ?

Node 1

Node 2

Node 3

Node 4

Query

QueryQuery

Query

Query

Query

Node 4: I have it!

Node 2: I have it!Node 4: Node 4 has it too!Reply

Reply

2004


Our solution: NeuroSearch• NeuroSearch resource discovery algorithm uses neural

networks and evolution to adapt its behavior to given environment– neural network for deciding whether to pass the query further

down the connection or not– evolution for breeding and finding out the best neural

network in a large class of local search algorithms• To authors’ knowledge this is the first time when neural

networks are being applied to resource discovery problem

Query

Forward the query

Forward the query

Neighbor Node

Neighbor Node

2004


NeuroSearch’s Inputs• The internal structure of NeuroSearch algorithm

• Multiple layers enable the algorithm to express non-linear behavior

• With enough neurons the algorithm can universally approximate any decision function

Tanh

Tanh

Threshold

2004


NeuroSearch’s Training Program

• The 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 theP2P networkconditions

Define the fitness requirements

for the algorithm

Create candidate algorithmsrandomly

Select the bestones for next

generation

Breed a newpopulation

Finally select thebest algorithm forthese conditions

Iteratethousands

ofgenerations

Compare the bestone against

Breadth-First Search

2004


Well How Good Is The Algorithm?

• We defined a peer-to-peer network scenario where:– 100 nodes form a power-law distributed P2P

network having few hubs and lots of low-connectivity nodes

– Resources are distributed based on the number of connections the node has meaning that high-connectivity nodes are more likely to answer to the queries

– Topology is static so the nodes are not moving• Then we defined a fitness function for the algorithm stating that:

– An algorithm that stops is always better than algorithm that does not

– The algorithm should locate half of the available resources for each query

– The algorithm should use as minimal number of packets as possible

2004


Well How Good Is The Algorithm?• After two weeks we were ready to compare NeuroSearch’s

invention against Breadth-First Search in 100-query test scenario

• The measurements indicate that the optimization process had developed an algorithm that:

– finds half of the resources in the network with high probability

– is more efficient than BFS with maximum number of three hops (BFS-3) and as efficient as BFS-2 while still locating the required 50% of resources

– has stable performance regardless of where the querier is located

Conclusion is that the approach is feasible, but not yet optimal

Algorithm Packets Resources Resources/Packets (Efficiency)

BFS-2 3000 619 (37,1%) 0.2063

BFS-3 12202 1295 (66,7%) 0.1061

NeuroSearch 4719 975 (53,2%) 0.2066

2004


Evolution Of Neural Networks

The best neuralnetwork of 85,736th

generation was selected for testing

2004


Performance of NeuroSearch – Hit Rate

NeuroSearch slightly misses the target of 50% resources in 8 queries

2004


Performance of NeuroSearch - Resources

BFS locates more resources when query starts from central nodes

2004


Performance of NeuroSearch - Packets

NeuroSearch is stable and the performance does not depend on where the query is started

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Typical query pattern of NeuroSearch

The maximum number of hops is 5

2004


Future Work

• Now the first version of NeuroSearch is ready and analyzed• The future work of NeuroSearch includes:

– Analysis of the effects of varying neural network’s structure• New input types to feed NeuroSearch with more

information• Adjusting the number of neurons to allow NeuroSearch to

make wiser decisions– Studying the scalability factors affecting NeuroSearch when

the P2P network size grows– Developing an optimal resource discovery algorithm using

global knowledge to be able to measure the best efficiency resource discovery algorithm can achieve

– Speeding up the optimization process by parallelizing evolutionary algorithm using distributed computing


Thank You!

Any questions?

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UNIVERSITY OF JYVÄSKYLÄ Resource Discovery in P2P Networks Using Evolutionary Neural Networks Presentation for International Conference on Advances in