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

A simulation study on congestion control for the ATM ABR service

Ankara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 1997.Thesis (Master's) -- Bilkent University, 1997.Includes bibliographical references leaves 75-78.Ülkü, SezerM.S

Design of traffic shaper / scheduler for packet switches and DiffServ networks : algorithms and architectures

The convergence of communications, information, commerce and computing are creating a significant demand and opportunity for multimedia and multi-class communication services. In such environments, controlling the network behavior and guaranteeing the user\u27s quality of service is required. A flexible hierarchical sorting architecture which can function either as a traffic shaper or a scheduler according to the requirement of the traffic load is presented to meet the requirement. The core structure can be implemented as a hierarchical traffic shaper which can support a large number of connections with a wide variety of rates and burstiness without the loss of the granularity in cells\u27 conforming departure time. The hierarchical traffic shaper can implement the exact sorting scheme with a substantial reduced memory size by using two stages of timing queues, and with substantial reduction in complexity, without introducing any sorting inaccuracy. By setting a suitable threshold to the length of the departure queue and using a lookahead algorithm, the core structure can be converted to a hierarchical rateadaptive scheduler. Based on the traffic load, it can work as an exact sorting traffic shaper or a Generic Cell Rate Algorithm (GCRA) scheduler. Such a rate-adaptive scheduler can reduce the Cell Transfer Delay and the Maximum Memory Occupancy greatly while keeping the fairness in the bandwidth assignment which is the inherent characteristic of GCRA. By introducing a best-effort queue to accommodate besteffort traffic, the hierarchical sorting architecture can be changed to a near workconserving scheduler. It assigns remaining bandwidth to the best-effort traffic so that it improves the utilization, of the outlink while it guarantees the quality of service requirements of those services which require quality of service guarantees. The inherent flexibility of the hierarchical sorting architecture combined with intelligent algorithms determines its multiple functions. Its implementation not only can manage buffer and bandwidth resources effectively, but also does not require no more than off-the-shelf hardware technology. The correlation of the extra shaping delay and the rate of the connections is revealed, and an improved fair traffic shaping algorithm, Departure Event Driven plus Completing Service Time Resorting algorithm, is presented. The proposed algorithm introduces a resorting process into Departure Event Driven Traffic Shaping Algorithm to resolve the contention of multiple cells which are all eligible for transmission in the traffic shaper. By using the resorting process based on each connection\u27s rate, better fairness and flexibility in the bandwidth assignment for connections with wide range of rates can be given. A Dual Level Leaky Bucket Traffic Shaper(DLLBTS) architecture is proposed to be implemented at the edge nodes of Differentiated Services Networks in order to facilitate the quality of service management process. The proposed architecture can guarantee not only the class-based Service Level Agreement, but also the fair resource sharing among flows belonging to the same class. A simplified DLLBTS architecture is also given, which can achieve the goals of DLLBTS while maintain a very low implementation complexity so that it can be implemented with the current VLSI technology. In summary, the shaping and scheduling algorithms in the high speed packet switches and DiffServ networks are studied, and the intelligent implementation schemes are proposed for them

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

Satellite Networks: Architectures, Applications, and Technologies

Since global satellite networks are moving to the forefront in enhancing the national and global information infrastructures due to communication satellites' unique networking characteristics, a workshop was organized to assess the progress made to date and chart the future. This workshop provided the forum to assess the current state-of-the-art, identify key issues, and highlight the emerging trends in the next-generation architectures, data protocol development, communication interoperability, and applications. Presentations on overview, state-of-the-art in research, development, deployment and applications and future trends on satellite networks are assembled

Resource allocation and congestion control strategies for networked unmanned systems

It is generally agreed that communication is a critical technological factor in designing networked unmanned systems (NUS) that consist of a large number of heterogeneous assets/nodes that may be configured in ad-hoc fashion and that incorporate intricate architectures. In order to successfully carry out the NUS missions, communication among assets need to be accomplished efficiently. In contrast with conventional networks, NUSs have specific features that may render communication more complex. The main distinct characteristics of NUS are as follows: (a) heterogeneity of assets in terms of resources, (b) multiple topologies that can be fully-connected, (c) real-time requirements imposed by delivery timeliness of messages under evolving and uncertain environments, (d) unknown and random time-delays that may degrade the closed-loop dynamics performance, (e) bandwidth constraints reflecting differences in assets behavior and dynamics, and (f) protocol limitations for complying with the wireless features of these networks. The NUS system consists of clusters each having three nodes, namely, a sensor, a decision-maker, and an actuator. Inspired by networked control systems (NCS), we introduced a generic framework for NUSs. Using the fluid flow model (FFM), the overall dynamical model of our network cluster is derived as a time-delay dependent system. The following three main issues are investigated in this thesis, bandwidth allocation, an integrated bandwidth allocation and flow rate control, and congestion control. To demonstrate the difficulty of addressing the bandwidth allocation control problem, a standard PID is implemented for our network cluster. It is shown that in presence of feedback loops and time-delays in the network, this controller induces flow oscillations and consequently, in the worst-case scenario, network instability. To address this problem, nonlinear control strategies are proposed instead. These strategies are evaluated subject to presence of unknown delays and measurable/estimated input traffic. For different network configurations, the error dynamics of the entire controlled cluster is derived and sufficient stability conditions are obtained. In addition, our proposed bandwidth allocation control strategy is evaluated when the NUS assets are assumed to be mobile. The bandwidth allocation problem is often studied in an integrated fashion with the flow rate control and the connection admission control (CAC). In fact, due to importance of interaction of various components, design of the entire control system is often more promising than optimization of individual components. In this thesis, several robust integrated bandwidth allocation and flow rate control strategies are proposed. The third issue that is investigated in this thesis is the congestion control for differentiated-services (DiffServ) networks. In our proposed congestion control strategies, the buffer queue length is used as a feedback information to control locally the queue length of each buffer by acting on the bandwidth and simultaneously a feedback signaling notifies the ordinary sources regarding the allowed maximum rate. Using sliding mode generalized variable structure control techniques (SM-GVSC), two congestion control approaches are proposed, namely, the non degenerate and degenerate GVS control approaches. By adopting decentralized end-to-end, semi-decentralized end-to-end, and distributed hop-by-hop control approaches, our proposed congestion control strategies are investigated for a DiffServ loopless mesh network (Internet) and a DiffServ fully-connected NUS. Contrary to the semi-decentralized end-to-end congestion control strategy, in the distributed hop-by-hop congestion control strategy, each output port controller communicates the maximum allowed flow rate only to its immediate upstream node(s) and/or source(s). This approach reduces the required amount of information in the flow control when Compared to other approaches in which the allowed flow rate is sent to all the upstream sources communicating through an output port

Design and analysis of flow control algorithms for data networks

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaves 110-112).by Paolo L. Naváez Guarnieri.M.S

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

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