In this paper, we propose a neural network (NN) approach for adaptive bandwidth allocation in ATM networks. This method is essentially based on the dynamic time-slice (DTS) scheme proposed by K. Sriram (1993) which guarantees a required bandwidth for each traffic class and/or virtual circuit (VC). Instead of using analytical static traffic tables to allocate bandwidth, we use NNs to adaptively estimate the effective bandwidths of different call types to reflect the time-varying nature of traffic conditions. Simulation results show that the neural estimation is more accurate and hence leads to higher resource utilization. The NN approach also provides faster response in reallocation of bandwidth to meet the stringent delay requirements.
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