Reinforcement learning for congestion-avoidance in packet flow

Tsuyoshi Horiguchi, Keisuke Hayashi and Alexei Tretiakov

Physica A: Statistical Mechanics and its Applications, 2005, vol. 349, issue 1, 329-348

Abstract: Occurrence of congestion of packet flow in computer networks is one of the unfavorable problems in packet communication and hence its avoidance should be investigated. We use a neural network model for packet routing control in a computer network proposed in a previous paper by Horiguchi and Ishioka (Physica A 297 (2001) 521). If we assume that the packets are not sent to nodes whose buffers are already full of packets, then we find that traffic congestion occurs when the number of packets in the computer network is larger than some critical value. In order to avoid the congestion, we introduce reinforcement learning for a control parameter in the neural network model. We find that the congestion is avoided by the reinforcement learning and at the same time we have good performance for the throughput. We investigate the packet flow on computer networks of various types of topology such as a regular network, a network with fractal structure, a small-world network, a scale-free network and so on.

Keywords: Congestion control; Reinforcement learning; Computer network; Mean-field approximation; Packet flow; Small-world network; Scale-free network (search for similar items in EconPapers)
Date: 2005
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:349:y:2005:i:1:p:329-348

DOI: 10.1016/j.physa.2004.10.015

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