Optimizing qos routing in hierarchical atm networks using computational intelligence techniques

In this paper, the use of a computational intelligence approach - a Reinforcement Learning Algorithm (RLA) - for optimizing the routing in asynchronous transfer mode (ATM) networks based on the private network-to-network interface (PNNI) standard is proposed. This algorithm which is specially designed for the quality of service (QoS) routing problem, aims at maximizing the network revenue (allocating efficiently the network resources) while ensuring the 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 commonly used routing schemes is demonstrated by means of simulation on existing commercial networks. 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.