A QoS-provisioning neural fuzzy connection admission controller for multimedia high-speed networks

This paper proposes a neural fuzzy approach for connection admission contro l (CAC) with QoS guarantee in multimedia high-speed networks. Fuzzy logic s ystems have been successfully applied to deal with traffic-control-related problems and have provided a robust mathematical framework for dealing with real-world imprecision. However, there is no clear and general technique t o map domain knowledge on traffic control onto the parameters of a fuzzy lo gic system. Neural networks have learning and adaptive capabilities that ca n be used to construct intelligent computational algorithms for traffic con trol. However, the knowledge embodied in conventional methods is difficult to incorporate into the design of neural networks. The proposed neural fuzz y connection admission control (NFCAC) scheme is an integrated method that combines the linguistic control capabilities of a fuzzy logic controller an d the learning abilities of a neural network. It is an intelligent implemen tation so that it can provide a robust framework to mimic experts' knowledg e embodied in existing traffic control techniques and can construct efficie nt computational algorithms for traffic control. We properly choose input v ariables and design the rule structure for the NFCAC controller so that it can have robust operation even under dynamic environments. Simulation resul ts show that compared with a conventional effective-bandwidth-based CAC, a fuzzy-logic-based CAC, and a neural-net-based CAC, the proposed NFCAC can a chieve superior system utilization, high learning speed, and simple design procedure, while keeping the QoS contract.