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27.9.2007 Annemari Auvinen, research student Department of Mathematical Information Technology

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

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27.9.2007 Annemari Auvinen, research student Department of Mathematical Information Technology

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  1. Topology Management in Unstructured P2P Networks Using NeuralNetworks 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. 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. 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. 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. NeuroTopology Neighbor Node Keep neighbor? P2P Node Neighbor’s neighbor New neighbor?

  6. Structure of NeuroTopology

  7. 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 theP2P network conditions Iteratethousandsofgenerations Create candidate algorithmsrandomly Select the bestones for nextgeneration Breed a newpopulation Define the fitness requirementsfor the algorithm Finally select thebest algorithm forthese conditions

  8. 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. 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. Simulation Environment • In the training set each generation is started with a grid topology P2P network and follows the algorithm: • Do 20 times • 10 random peers execute resource queries • Execute NeuroTopology algorithm in every peer using information from resource queries • Execute 10 resource queries in the P2P network • Calculate the fitness for the neural network using information from step 2

  11. 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. Fitness in training environment

  13. Fitness in generalization environment

  14. Resource query packets and replies in generalization environment

  15. Topology packets and changes in generalization environment

  16. Failed queries in generalization environment

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

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

  19. 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.

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