Application of learning algorithms to traffic management in integrated services networks.

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Application of an automatically designed fuzzy logic decision support system to connection admission control in ATM networks

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COMPARATIVE ANALYSIS OF NEURO- FUZZY AND SIMPLEX OPTIMIZATION MODEL FOR CONGESTION CONTROL IN ATM NETWORK.

Congestion always occurred when the transmission rate increased the data handling capacity of the network. Congestion normally arises when the network resources are not managed efficiently. Therefore if the source delivers at a speed higher then service rate queue, the queue size will be higher. Also if the queue size is finite, then the packet will observed delay. MATLAB Software was used to carry out simulations to develop Congestion control optimization Scheme for ATM Network with the aims to reducing the congestion of Enugu ATM Network. The results of the research reveal the minimization of congestion application model for Enugu ATM using optimization and Neuro-fuzzy. The result shows that congestion control model with Optimization and Neuro-fuzzy were 0.00003153 and 0.00002098 respectively. The ATM Congestion was reduced by 0.0000105, which is 18.2% decrease after Neuro-fuzzy controller was used. The results show the application of Neuro-fuzzy model which can use to control and minimized the ATM Congestion of Enugu ATM Network. The result shows that when Neuro-fuzzy is applied the congestion and the packet queue length in the buffer will be minimized. Key words: Congestion, MATLAB, Optimization, Neuro-fuzzy, ATM DOI: 10.7176/CTI/10-05 Publication date:July 31st 2020

Dynamic bandwidth allocation in ATM networks

Includes bibliographical references.This thesis investigates bandwidth allocation methodologies to transport new emerging bursty traffic types in ATM networks. However, existing ATM traffic management solutions are not readily able to handle the inevitable problem of congestion as result of the bursty traffic from the new emerging services. This research basically addresses bandwidth allocation issues for bursty traffic by proposing and exploring the concept of dynamic bandwidth allocation and comparing it to the traditional static bandwidth allocation schemes

Renegotiation based dynamic bandwidth allocation for selfsimilar VBR traffic

The provision of QoS to applications traffic depends heavily on how different traffic types are categorized and classified, and how the prioritization of these applications are managed. Bandwidth is the most scarce network resource. Therefore, there is a need for a method or system that distributes an available bandwidth in a network among different applications in such a way that each class or type of traffic receives their constraint QoS requirements. In this dissertation, a new renegotiation based dynamic resource allocation method for variable bit rate (VBR) traffic is presented. First, pros and cons of available off-line methods that are used to estimate selfsimilarity level (represented by Hurst parameter) of a VBR traffic trace are empirically investigated, and criteria to select measurement parameters for online resource management are developed. It is shown that wavelet analysis based methods are the strongest tools in estimation of Hurst parameter with their low computational complexities, compared to the variance-time method and R/S pox plot. Therefore, a temporal energy distribution of a traffic data arrival counting process among different frequency sub-bands is considered as a traffic descriptor, and then a robust traffic rate predictor is developed by using the Haar wavelet analysis. The empirical results show that the new on-line dynamic bandwidth allocation scheme for VBR traffic is superior to traditional dynamic bandwidth allocation methods that are based on adaptive algorithms such as Least Mean Square, Recursive Least Square, and Mean Square Error etc. in terms of high utilization and low queuing delay. Also a method is developed to minimize the number of bandwidth renegotiations to decrease signaling costs on traffic schedulers (e.g. WFQ) and networks (e.g. ATM). It is also quantified that the introduced renegotiation based bandwidth management scheme decreases heavytailedness of queue size distributions, which is an inherent impact of traffic self similarity. The new design increases the achieved utilization levels in the literature, provisions given queue size constraints and minimizes the number of renegotiations simultaneously. This renegotiation -based design is online and practically embeddable into QoS management blocks, edge routers and Digital Subscriber Lines Access Multiplexers (DSLAM) and rate adaptive DSL modems

Some aspects of traffic control and performance evaluation of ATM networks

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