Rate regulation with feedback controller in ATM networks-a neural network approach

We propose the use of an artificial neural network (ANN) technique for a rate-based feedback controller in asynchronous transfer mode (ATM) networks. A leaky bucket (LB) mechanism is used for cell discarding, when the traffic violates a predefined threshold. Since the network cannot rely on the user´s compliance with its declared parameters, it is extremely difficult to select the best threshold value and depletion rate for the LB. We propose an ANN model which monitors the status of the LB and predicts the possible cell discarding at the LB in the near future. The source rate is regulated to a certain amount depending on the feedback signal “strength” when possible cell discarding is detected. The lower the value carried in the feedback cell, the higher the possibility of cell discarding and, subsequently, the higher the probability that the traffic is regulated to a lower rate. Our model considers the propagation delay time of the feedback signal making our approach more realistic. This mechanism is transparent to the source if the LB is correctly set up and the traffic follows its declared parameters. We use the same trained ANN for different MPEG traces and the results of a simulation study suggest that our mechanisms provide simple and effective traffic management for ATM networks. Cell loss rate due to the congestion shows a two to five times improvement compared with the static approach, while transmission delays introduced by our ANN controller are also smaller than in the static approach. Channel utilization is also improved, showing that our mechanisms provides a better alternative to static feedback controllers