A power-spectrum based neural fuzzy connection admission mechanism for ATM networks

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.