1. UNIVERSITY OF JYVÄSKYLÄ Resource Discovery Using NeuroSearch Presentation for the Agora Center InBCT-seminar 13.11.2003 Mikko Vapa, researcher InBCT 3.2 Cheese Factory / P2P Communication Agora Center http://tisu.it.jyu.fi/cheesefactory

2. UNIVERSITY OF JYVÄSKYLÄ 2003 Resource Discovery Problem In peer-to-peer (P2P) resource discovery problem any node in the network can possess resources and also query these resources from other nodes Node1: Where is ? Node 1 Node 2 Node 3 Node 4

3. UNIVERSITY OF JYVÄSKYLÄ 2003 Resource Discovery Problem The problem is multi-dimensional because: –Location of resources can be arbitrary, it is not known beforehand and often changes constantly –Topology can be arbitrary and it might also change between each query –The querying node’s location in the network is arbitrary –Sufficient number of replies might depend on the queried resource It is enough to locate one instance of a file or a processor, but the performance usually speeds linearly up when multiple instances are located –Some of the nodes might not behave as expected For example some of the queries might be dropped by adversarial nodes requiring multiple query paths to be used When the number of dimensions increase problem becomes complex

4. UNIVERSITY OF JYVÄSKYLÄ 2003 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 2003 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 link or not –evolution for breeding and finding out the best neural network in a large class of local search algorithms Query Forward the query Neighbor Node

6. UNIVERSITY OF JYVÄSKYLÄ 2003 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

7. UNIVERSITY OF JYVÄSKYLÄ 2003 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 the 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 other local search algorithms

8. UNIVERSITY OF JYVÄSKYLÄ 2003 Benefits Universal: Whatever the peer-to-peer network conditions are a feasible solution algorithm can be found Zero-configurable: There is no parameters that a designer would need to tune by hand Supports various requirements: Designer can define what kind of an algorithm he/she wants to have Rapid development: Even designing a simple algorithm for P2P network might take many months by human designer while with NeuroSearch this time is only couple of hours Efficient: Multiple search strategies may in some cases be the only viable choice for example in mobile peer-to-peer networks where the environment is changing all the time

9. UNIVERSITY OF JYVÄSKYLÄ 2003 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 meaning that nodes are not moving Then we made a wish list for the algorithm and hoped that: –The algorithm should locate half of the available resources for every query –The algorithm should use as minimal number of packets as possible –The algorithm should always stop

10. UNIVERSITY OF JYVÄSKYLÄ 2003 Well How Good Is The Algorithm? After a week we were ready to compare NeuroSearch’s invention against Breadth-First Search in 50-query test scenario The measurements indicate that the optimization process can find an algorithm that: –finds half of the resources in the network with high probability –locates more resources than BFS with maximum number of 2 hops (BFS-2), but sill fewer than BFS-3 –consumes a bit more packets than BFS-2, but significantly less than BFS-3 –adapts to the peer-to-peer environment taking advantage of the environments resource distributions and topological features  Conclusion is that the approach is feasible, but not yet optimal

11. UNIVERSITY OF JYVÄSKYLÄ 2003 Performance of NeuroSearch – Hit Rate

12. UNIVERSITY OF JYVÄSKYLÄ 2003 Performance of NeuroSearch - Replies

13. UNIVERSITY OF JYVÄSKYLÄ 2003 Performance of NeuroSearch - Packets

14. UNIVERSITY OF JYVÄSKYLÄ 2003 Typical query pattern of NeuroSearch

15. UNIVERSITY OF JYVÄSKYLÄ 2003 Future Work Now the first version of NeuroSearch is ready and analyzed The short-range future work of NeuroSearch includes (master’s thesis topics): –Comparison to other existing resource discovery algorithms –Reduction of resource discovery problem to route discovery problem and analysis of behavior in dynamic conditions –Analysis of the effects of increasing NeuroSearch’s brain power and peer-to-peer network size New input types to feed NeuroSearch with more information More neurons to allow NeuroSearch to make wiser decisions Studying the scalability factors affecting NeuroSearch when the network size grows –Hybridization of evolutionary optimization method with local optimization method

16. UNIVERSITY OF JYVÄSKYLÄ 2003 Future Work The long-range future work of NeuroSearch includes: –Parallelization of evolutionary optimization method for speeding up the convergence time (free master’s thesis topic) –Multicriteria objective functions for NeuroSearch for maximizing the lifetime of battery powered mobile P2P networks (free master’s thesis topic) –Development of new ad hoc protocol based on NeuroSearch (free dissertation topic) –Combination of topology management algorithm with NeuroSearch for finding optimal P2P network (dissertation topic) –Extension of NeuroSearch to ontology-based queries and reduction of query traffic using varying ontologies (dissertation topic) –Study of NeuroSearch’s performance under attack, random failure and deceptive scenarios (free dissertation topic) –Online adaptation of NeuroSearch in real P2P environment (free dissertation topic)

17. UNIVERSITY OF JYVÄSKYLÄ 2003 References Vapa M., Kotilainen N., Kainulainen H., Vuori J., ”Resource Discovery in P2P Networks Using Evolutionary Neural Networks”, submitted to IEE Electronics Letters, June 2003 Vapa M., Kotilainen N., Auvinen A., Töyrylä J., Hyytiälä H., Vuori J., ”NeuroSearch: evolutionary neural network resource discovery algorithm for peer-to-peer networks”, being submitted to Elsevier Science Ad Hoc Networks Journal, November 2003

18. UNIVERSITY OF JYVÄSKYLÄ Thank You! Any questions?