1. 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

2. 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

3. UNIVERSITY OF JYVÄSKYLÄ 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

4. UNIVERSITY OF JYVÄSKYLÄ 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

5. UNIVERSITY OF JYVÄSKYLÄ NeuroTopology Keep neighbor? New neighbor? Neighbor Node Neighbor’s neighbor P2P Node

6. UNIVERSITY OF JYVÄSKYLÄ Structure of NeuroTopology

7. UNIVERSITY OF JYVÄSKYLÄ 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

8. UNIVERSITY OF JYVÄSKYLÄ 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]

9. UNIVERSITY OF JYVÄSKYLÄ 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

10. UNIVERSITY OF JYVÄSKYLÄ 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

11. UNIVERSITY OF JYVÄSKYLÄ 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

12. UNIVERSITY OF JYVÄSKYLÄ Fitness in training environment

13. UNIVERSITY OF JYVÄSKYLÄ Fitness in generalization environment

14. UNIVERSITY OF JYVÄSKYLÄ Resource query packets and replies in generalization environment

15. UNIVERSITY OF JYVÄSKYLÄ Topology packets and changes in generalization environment

16. UNIVERSITY OF JYVÄSKYLÄ Failed queries in generalization environment

17. UNIVERSITY OF JYVÄSKYLÄ Simulation Results Tested in grid topology, power-law topology and a random graph topology with 3 resource discovery algorithms and with and without NeuroTopology

18. UNIVERSITY OF JYVÄSKYLÄ Convergence Changing the inefficient grid topology on the early rounds and limiting the changes when the efficient topology has been reached

19. UNIVERSITY OF JYVÄSKYLÄ 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.