Reinforcement learning for congestion-avoidance in packet flow
Reinforcement learning for congestion-avoidance in packet flow
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DOI：
10.1016/j.physa.2004.10.015
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发表时间：
2005-04-01
影响因子：
3.3
通讯作者：
Tretiakov, A
中科院分区：
文献类型：
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作者：
Horiguchi, T;Hayashi, K;Tretiakov, A
计算机网络中数据包流拥塞的发生是数据包通信中的一个不利问题，因此必须研究如何避免拥塞。我们使用的神经网络模型的数据包路由控制在计算机网络中提出的Horiguchi和石冈（物理A 297（2001）521）在以前的文件。如果我们假设数据包没有被发送到缓冲区已经充满数据包的节点，那么我们发现当计算机网络中的数据包数量大于某个临界值时，就会发生流量拥塞。为了避免拥塞，我们在神经网络模型中引入了一个控制参数的强化学习。我们发现，拥塞是避免强化学习，同时我们有良好的性能的吞吐量。我们研究了各种拓扑结构的计算机网络上的包流，如规则网络，分形结构的网络，小世界网络，无标度网络等。（C）2004 Elsevier B.V.版权所有。
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. (C) 2004 Elsevier B.V. All rights reserved.