Multi-service connection admission control using modular neural networks

Abstract

Although neural networks have been applied for traffic and congestion control in ATM networks, most implementations use multi-layer perception (MLP) networks which are known to converge slowly. In this paper, we present a Connection Admission Control (CAC) scheme which uses a modular neural network with fast learning ability to predict the cell loss ratio (CLR) at each switch in the network. A special type of OAM cell travels from the source node to the destination node and back in order to gather information at each switch. This information is used at the source to make CAC decisions such that Quality of Service (QoS) commitments are not violated. Experimental results which compare the performance of the proposed method with other CAC methods which use the Peak Cell Rate (PCR), Average Cell Rate (ACR) and Equivalent Bandwidth are presented.