Data networks
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
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 packet routing method for a computer network using chaotic neurodynamics. We first compose a basic neural network which routes packets using information of shortest path lengths from a node to the other nodes. When the computer network topology is regular, the routing method works well, however, when the computer network topology becomes irregular, the basic routing method doesn't work well. The reason is that most of packets cannot be transmitted to their destinations because of packet congestion in the computer network. To avoid such an undesirable problem, we extended the basic method to employ chaotic neurodynamics. We confirm that our proposed method exhibits good performance for computer networks with various topologies. Furthermore, we analyze why the proposed routing method is effective: we introduce the method of surrogate data which is often used in the field of nonlinear time-series analysis. In consequence of introducing such a statistical control, we confirm that using chaotic neurodynamics is the most effective policy to decentralize the congestion of the packets in the computer network.