1. 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 student Agora Center http://tisu.it.jyu.fi/cheesefactory With co-authors Niko Kotilainen, Annemari Auvinen, Heikki Kainulainen and Jarkko Vuori

2. UNIVERSITY OF JYVÄSKYLÄ 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

3. UNIVERSITY OF JYVÄSKYLÄ 2004 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

4. UNIVERSITY OF JYVÄSKYLÄ 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 Node 4: I have it! Node 2: I have it! Node 4: Node 4 has it too! Reply

5. UNIVERSITY OF JYVÄSKYLÄ 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 Neighbor Node

6. UNIVERSITY OF JYVÄSKYLÄ 2004 NeuroSearchs 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 Threshold

7. UNIVERSITY OF JYVÄSKYLÄ 2004 NeuroSearchs 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 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 Compare the best one against Breadth-First Search

8. UNIVERSITY OF JYVÄSKYLÄ 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

9. UNIVERSITY OF JYVÄSKYLÄ 2004 Well How Good Is The Algorithm? After two weeks we were ready to compare NeuroSearchs 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 AlgorithmPacketsResourcesResources/Packets (Efficiency) BFS-23000619 (37,1%)0.2063 BFS-3122021295 (66,7%)0.1061 NeuroSearch4719975 (53,2%)0.2066

10. UNIVERSITY OF JYVÄSKYLÄ 2004 Evolution Of Neural Networks The best neural network of 85,736 th generation was selected for testing

11. UNIVERSITY OF JYVÄSKYLÄ 2004 Performance of NeuroSearch – Hit Rate NeuroSearch slightly misses the target of 50% resources in 8 queries

12. UNIVERSITY OF JYVÄSKYLÄ 2004 Performance of NeuroSearch - Resources BFS locates more resources when query starts from central nodes

13. UNIVERSITY OF JYVÄSKYLÄ 2004 Performance of NeuroSearch - Packets NeuroSearch is stable and the performance does not depend on where the query is started

14. UNIVERSITY OF JYVÄSKYLÄ 2004 Typical query pattern of NeuroSearch The maximum number of hops is 5

15. UNIVERSITY OF JYVÄSKYLÄ 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 networks 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

16. UNIVERSITY OF JYVÄSKYLÄ Thank You! Any questions?