A variable rate leaky bucket algorithm based on a neural network prediction in ATM networks

To tackle the problem of congestion control due to the bursty nature of various traffic sources in ATM networks, UPC/NPC (user parameter control/network parameter control) have been actively studied. The DRLB (dynamic rate leaky bucket) algorithm, in which the token generation rate is dynamically changed according to states of the data source and buffer occupancy, is a good example of the UPC/NPC. However, the DRLB algorithm has several drawbacks such as low efficiency and difficult real-time implementation for bursty traffic sources, because the determination of the token generation rate in the algorithm is based on the present state of the network. We propose a more plastic and effective congestion control algorithm by combining the DRLB algorithm and neural network based prediction to remedy the drawbacks of the DRLB algorithm, and verify the efficacy of the proposed method by computer simulations