In this dissertation, new and efficient traffic management schemes based on fuzzy logic for ABR services in ATM have been suggested. End-to-end closed loop feedback control of the source rates has been adopted for ABR traffic control method. The cell-transmission-rates are regulated according to the explicit rate values calculated from the network. Though many control algorithms have been proposed so far, most of them can lead to unfairness problems unless their control parameters are set properly. In addition, large time-delay incurred in the feedback path and the statistical variation in the link capacity at the ATM node can cause buffer saturation or unfairness. The control algorithm should not interfere with delay-sensitive traffic to assure given QoS and minimal cell loss.
We propose two adaptive fuzzy explicit rate allocation algorithms which are composed of linguistic rules and fuzzy inference engine for deciding explicit rates of all sources. The parameters of the fuzzy rules are adapted to minimize the given performance index in both cases. The controllers estimate future queue length from the previous input loads while it monitors current buffer length, degree of fairness, link-utilization and available bandwidth. The level of network congestion is monitored through the occupancy q of the ABR buffer, with the control target being set at a certain threshold $q_d$.
Between two algorithms, one mechanism is based on a heuristic algorithm. The performance index is composed of difference between q and $q_d$ and fairness degree of each connection allocated the available bandwidth. The other mechanism is based on the projection algorithm. In this, the stability of the overall control system is analyzed and proved. Theses new traffic management approaches have both adaptive and learning behaviors without any assumption on the traffic pattern. The performances of the proposed control mechanisms are validated for various ABR and VBR demand patterns. It is noti...