Abstract
In this chapter the use of a Reinforcement Learning Algorithm for optimizing the routing in Asynchronous Transfer Mode (ATM) networks based on the Private Network-to-Network Interface (PNNI) standard is proposed. This algorithm aims at maximizing the network throughput (allocating efficiently the network resources) while guaranteeing the Quality of Service (QoS) requirements for each connection. In this study, large-scale networks are considered where it becomes necessary to be organized hierarchically so that a scale in terms of computation, communication, and storage requirements will be achieved. A comparative performance study of the proposed and other well-known routing schemes is demonstrated by means of simulation on an existing commercial network. Simulation results over a wide range of uniform, time-varying, and skewed loading conditions show the effectiveness of the proposed routing algorithm, and disclose the strength and weakness of the various schemes.
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