Automatically designed fuzzy system for connection admission control in ATM networks

Connection admission control (CAC) is a vital function for asynchronous transfer mode networks. A CAC algorithm should be simple, i.e. economically implementable and fast, and it should be efficient, i.e. allow statistical multiplexing. A solution based on an analytical queueing model is too CPU-intensive and cannot be applied online. The paper proposes a new scheme based on fuzzy logic. The aim is to predict online the cell loss ratio that a connection will exhibit if it is accepted into the network. The CAC scheme is based on a consideration of fuzzy logic and artificial neural networks (ANNs). The ANN is used in the learning phase to tune the fuzzy system automatically. The structure of the neuro-fuzzy system is discussed. A training set obtained by an analytical algorithm, namely the convolution algorithm, is used to develop a learning algorithm and to check the applicability of the technique.