A novel neural network traffic enforcement mechanism for ATM networks

The asynchronous transfer mode (ATM) principle has been recommended by the CCITT as the transport vehicle for the future broadband ISDN networks. In ATM-based networks, a set of user declared parameters that describes the traffic characteristics, is required for the connection acceptance control (CAC) and traffic enforcement (policing) mechanisms. During the call progress, the policing mechanism uses those parameters to control the user's traffic within its declared values in order to protect the network's resources and avoid possible congestion problems. In this paper, a novel policing mechanism using neural networks (NNs) is presented. The mechanism is based upon an accurate estimation of the probability density function (PDF) of the traffic via its count process and implemented using NNs. The architecture of the policing mechanism is composed of two inter-connected NNs. The first one is trained to learn the PDF of an "ideal non-violating" traffic, whereas the second is trained to capture the "actual" characteristics of the "actual" offered traffic during the progress of the call. The output of both NNs is compared. Consequently, an error signal is generated whenever the PDF of the offered traffic violates its "ideal" one. The error signal is then used to shape the traffic back to its original values. The reported results, prove that our policing mechanism is very effective in detecting (and policing) all possible kinds of traffic violations.<<ETX>>