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The emerging high-speed Asynchronous Transfer Mode (ATM) networks are expected to integrate through statistical multiplexing large numbers of traffic sources having a broad range of statistical characteristics and different Quality of Service (QOS) requirements. To achieve high utilisation of network resources while maintaining the QOS, efficient traffic management strategies have to be developed. This thesis considers the problem of traffic control for ATM networks. The thesis studies the application of neural networks to various ATM traffic control issues such as feedback congestion control, traffic characterization, bandwidth estimation, and Call Admission Control (CAC). A novel adaptive congestion control approach based on a neural network that uses reinforcement learning is developed. It is shown that the neural controller is very effective in providing general QOS control. A Finite Impulse Response (FIR) neural network is proposed to adaptively predict the traffic arrival process by learning the relationship between the past and future traffic variations. On the basis of this prediction, a feedback flow control scheme at input access nodes of the network is presented. Simulation results demonstrate significant performance improvement over conventional control mechanisms. In addition, an accurate yet computationally efficient approach to effective bandwidth estimation for multiplexed connections is investigated. In this method, a feed forward neural network is employed to model the nonlinear relationship between the effective bandwidth and the traffic situations and a QOS measure. Applications of this approach to admission control, bandwidth allocation and dynamic routing are also discussed. A detailed investigation has indicated that CAC schemes based on effective bandwidth approximation can be very conservative and prevent optimal use of network resources. A modified effective bandwidth CAC approach is therefore proposed to overcome the drawback of conventional methods. Considering statistical multiplexing between traffic sources, we directly calculate the effective bandwidth of the aggregate traffic which is modelled by a two-state Markov modulated Poisson process via matching four important statistics. We use the theory of large deviations to provide a unified description of effective bandwidths for various traffic sources and the associated ATM multiplexer queueing performance approximations, illustrating their strengths and limitations. In addition, a more accurate estimation method for ATM QOS parameters based on the Bahadur-Rao theorem is proposed, which is a refinement of the original effective bandwidth approximation and can lead to higher link utilisation.

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Thesis (Ph.D.)--University of Western Australia, 2004.

Thesis (Ph. D.)--University of Washington, 1997. / Vita. Includes bibliographical references (leaves [138]-144).

Congestion in ATM communications is a significant issue as it can have a dramatic effect on critical or real-time data. Forward Error Correction (FEC) codes are one class of protocols to decrease this effect. Conventional FEC techniques have a uniform or constant error correction rate, which can result in poor bandwidth utilization. Therefore adaptive techniques are sought. The rationale is to have better bandwidth utilization when congestion occurs. In this thesis, we investigate the related work on FEC in ATM networks. Then we propose an adaptive FEC scheme based on RS codes. This proposed scheme is then studied in different types of environments, wireline and wireless. Simulations are also conducted to measure different performance issues concerning network resources and quality of service. Another crucial issue in ATM communications is security. The proposed FEC scheme has an added feature of being security ready. Moreover it has been shown that the security scheme is computationally secure. Such FEC scheme has significant impact on ATM network resources and switch capacity. This has been investigated further in this work. Switch architectures utilizing FEC schemes are also studied. / Graduate

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