A novel neural network traffic enforcement mechanism for ATM networks

Abstract
ATM has been recommended by the CCITT as the transport vehicle for the future B-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. At the call set-up phase, the CAC algorithm uses those parameters to make a call acceptance decision. During the call progress, the policing mechanism uses the same parameters to control the user's traffic within its declared values in order to protect the network's resources and avoid possible congestion problems. A novel policing mechanism using neural networks (NNs) is presented. This is based upon an accurate estimation of the probability density function (pdf) of the traffic via its count process and implemented using NNs. The pdf-based policing is made possible only by NNs because pdf policing requires complex calculations, in real-time, at very high speeds. The architecture of the policing mechanism is composed of two interconnected NNs. The first one is trained to learn the pdf of “ideal nonviolating” 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

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