ATM (asynchronous transfer mode) networks support services with bursty traffic. A sophisticated and real-time admission controller is needed not only to guarantee the required quality-of-service (QoS) for existing calls but also to achieve higher system efficiency. In this paper, we propose a power-spectrum based neural fuzzy connection admission control (PNFCAC) mechanism for an ATM network. The power spectrum of the input process explicitly describes the correlation behavior of input traffic and has a great impact on the queueing behavior of an ATM network. The neural fuzzy controller has been successfully applied to deal with the complex problem which can not be analyzed by classical methods based on probability theory and bivalent logic. Under the constraint of QoS, we construct a decision hyperplane of connection admission control according to the parameters of the power spectrum. We further adopt the learning/adapting capabilities of the neural network to adjust the optimum location of the boundary between these two decision spaces. Simulation results show that the PNFCAC mechanism provides a superior system utilization over the conventional CAC schemes and the neural fuzzy CAC using time-domain analysis, while keeping the QoS contract.

ATM (asynchronous transfer mode) systems can support services with bursty traffic. An ATM system needs a sophisticated and real-time connection admission controller not only to guarantee the required quality-of-service (QoS) for existing calls but also to raise the system efficiency. The input process has a power-spectral-density (PSD) which explicitly contains the correlation behavior of input traffic and has a great impact on the system performance. Also, a neural network has been widely applied to deal with traffic control related problems in ATM systems because of its self-learning capability. We propose a PSD-based neural-net connection admission control (PNCAC) method for an ATM system. Under the QoS constraint, we construct a decision hyperplane of the connection admission control according to parameters of the power spectrum. We further adopt the learning/adapting capabilities of the neural network to adjust the optimum location of the boundary between these two decision spaces. Simulation results show that the PNCAC method provides a superior system utilization over the conventional CAC schemes by as much as 18%, while keeping the QoS contract.