In this paper, we propose two intelligent leaky bucket algorithms for sustainable-cell-rate usage parameter control of multimedia transmission in asynchronous transfer mode networks. One is the fuzzy leaky bucket algorithm, in which a fuzzy increment controller (FIC) is incorporated with the conventional leaky bucket algorithm; the other is the neural fuzzy leaky bucket algorithm, where a neural fuzzy increment controller (NFIC) is added with the conventional leaky bucket algorithm. Both the FIC and the NFIC properly choose the long-term mean cell rate and the short-term mean cell rate as input variables to intelligently determine the increment value. Simulation results show that both intelligent leaky bucket algorithms have significantly outperformed the conventional leaky bucket algorithm, by responding about 160% faster when taking control actions against a nonconforming connection while reducing as much as 50% of the queueing delay experienced by a conforming connection. In addition, the neural fuzzy leaky bucket algorithm outperforms the fuzzy leaky bucket algorithm, in aspects of three performance measures such as selectivity, responsiveness, and queueing delay, especially when the traffic flow is bursty, dynamic, and nonstationary.

In this paper, we propose a traffic conditioner with promotion and fairness guarantee (TC/spl I.bar/PFG) schemes to amend the inappropriate marking by conventional traffic conditioners. The TC/spl I.bar/PFG is based on the two rate three color marker (TRTCM) scheme. It can allocate appropriate bandwidth to micro-flow to reduce the unfair distribution among micro-flows and improve the utilization by allowing promotion of packets when there is available bandwidth. Simulation results show that TC/spl I.bar/PFG outperforms TRTCM; the former achieve higher fairness among micro-flows and better throughput than the latter.

This paper proposes a neural fuzzy approach for connection admission control (CAC) with QoS guarantee in multimedia high-speed networks. Fuzzy logic systems 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 to map domain knowledge on traffic control onto the parameters of a fuzzy logic system. Neural networks have learning and adaptive capabilities that can be used to construct intelligent computational algorithms for traffic control. However, the knowledge embodied in conventional methods is difficult to incorporate into the design of neural networks. The proposed neural fuzzy connection admission control (NFCAC) scheme is an integrated method that combines the linguistic control capabilities of a fuzzy logic controller and the learning abilities of a neural network. It is an intelligent implementation so that it can provide a robust framework to mimic experts' knowledge embodied in existing traffic control techniques and can construct efficient computational algorithms for traffic control. We properly choose input variables and design the rule structure for the NFCAC controller so that it can have robust operation even under dynamic environments. Simulation results 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 achieve superior system utilization, high learning speed, and simple design procedure, while keeping the QoS contract.