In this dissertation, the traffic control functions involving the connection admission control (CAC) and the traffic policing for multimedia high-speed networks by neural/fuzzy intelligent techniques are studied. Several types of service with different QoS requirements and various bandwidth demands have to be supported by the multimedia high-speed networks. Both ATM and IP networks which can be utilized to construct the multimedia high-speed networks are considered in this dissertation. The CAC schemes which make the admission control decisions according to the time-domain and frequency-domain traffic parameters are both discussed where the intelligent techniques are chosen to implement the CAC controllers.
Also, the enhanced algorithms which implement the traffic policing function by incorporating the intelligent techniques and a elaborate computation procedure into existing algorithms for ATM and IP networks respectively are both well explored.
In Chapter 2, the basic concepts of fuzzy systems, neural networks, and integrated neural fuzzy systems are briefly reviewed. The architecture of a fuzzy inference system (FIS) and the most basic and popular fuzzy inference model to implement a fuzzy logic controller are stated. The neural networks and learning mechanism are presented along with two popular
architectures for implementing a neural network controller. The benefits of integrated neural fuzzy systems is described. Also a typical five-layer connectionist architecture to build a neural fuzzy controller are stated there. Additionally, the applications of these intelligent techniques to the traffic control functions over multimedia high-speed networks are given.
In Chapter 3, a neural fuzzy connection admission control (NFCAC) scheme which based on the time-domain traffic parameters and provides QoS guarantees for multimedia high-speed ATM networks is proposed. The NFCAC scheme adopts a neural fuzzy controller for admission control, which integrates the linguistic control capabilities of a fuzzy logic con-troller with the learning abilities of a neural network. We properly choose input variables which involves the measured statistics of network performances and the available network resources converted from the time-domain traffic parameters, and then well design the rule structure for the neural fuzzy controller. Accordingly, the NFCAC scheme can provide a robust framework to mimic experts’ knowledge embodied in existing connection admission control techniques and can construct precise and efficient computational algorithms for con-nection admission control to achieve high system utilization while supporting QoS-guarantee.
In Chapter 4, a power-spectrum-based neural-net connection admission control (PNCAC) scheme for multimedia high-speed ATM networks is proposed. It employs a neural network controller to handle the connection admission control function according to the frequency-domain power spectral density (PSD) parameters of the traffic sources. With a composition algorithm to easily obtain the approximated three PSD parameters of the virtually aggre-gated total traffic enrolling the new call request, the neural network controller accommodate all the three PSD parameters as the inputs and generate the admission control decision.
After well training the neural network, an optimal CAC decision hyperplane based on the input variables is constructed to provide an efficient and robust admission control even under dynamic network environments, while the QoS requirements are still satisfied and strictly
as-sured. Also, the learning capability of the neural network techniques can bring adaptability to respond to the network dynamics.
In Chapter 5, two intelligent usage parameter controllers are proposed to implement the traffic policing function for the sustainable-cell-rate (SCR) of multimedia transmissions in ATM networks. One is the fuzzy usage parameter controller realized by 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 usage parameter controller base on the neural fuzzy leaky bucket algorithm, where a neural fuzzy increment controller (NFIC) is added to the conventional leaky bucket algorithm. The FIC and NFIC are exactly the fuzzy logic controller and the neural fuzzy controller, respectively, and both of them properly choose two measured statistics of the network performances, the long-term mean cell rate and the short-term mean cell rate, as the input variables to adaptively determine the optimal increment value with respect to the traffic dynamics. Accordingly, both of the proposed fuzzy and neural fuzzy usage parameter controllers can achieve better performances than the conventional leaky-bucket-based usage parameter controller because of the dynamic increment value by adaptive decisions.
In Chapter 6, an enhanced traffic marker (ETM) based on the Two-Rate-Three-Color-Marker (TRTCM) scheme is proposed for the traffic conditioner to perform traffic policing by properly determining the conforming level of the incoming packet and making a corre-sponding color notation on the packet. The proposed ETM scheme introduces the features of aggressive promotion and fair share marking, and incorporates them into the existing traffic policing function. One of the primary performance objectives is that it can fairly allocate the color notations among connections within an aggregate one. It is also anticipated to enhances the throughput of each conforming level for the aggregate connection to achieve as high rate as possible by not only restore the conforming levels of the previously demoted
packets, but also aggressively promote the packets to higher conforming levels if the net-work resource condition is available, so that the end-to-end QoS of the applications would be substantially improved while the traffic contract is still be respected. The performances of the proposed ETM scheme were verified via simulations and the simulation results were compared with the conventional TRTCM scheme.
Finally, some concluding remarks and future research topics are addressed in Chapter 7.