An optimum strategy for dynamic and stochastic packet routing problems by chaotic neurodynamics
Integrated Computer-Aided Engineering - Artificial Neural Networks
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We propose a new algorithm for routing packets which effectively avoids packet congestion in computer networks. The algorithm involves chaotic neurodynamics. To realize effective packet routing, we first composed a basic method by a neural network, which routes packets with shortest path information between two nodes in the computer network. When the computer network has an irregular topology, the basic routing method does not work well, because most of packets cannot be transmitted to their destinations due to packet congestion in the computer network. To avoid such an undesirable problem, we employed chaotic neurodynamics to extend the basic method. Numerical experiments show that our proposed method exhibits good performance for scale-free networks. We also analyze why the proposed routing method is effective, comparing the proposed method with several stochastic methods. We introduced the method of surrogate data, a statistical hypothesis testing which is often used in the field of nonlinear time-series analysis. Analysis of the proposed method by the method of surrogate data reveals that the chaotic neurodynamics is most effective to decentralize the packet congestion in the computer network.