Applications of neurocomputing in traffic management of ATM networks

Habib, I.

doi:10.1109/5.537109

Cited by 25 publications

(9 citation statements)

References 25 publications

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“…The larger the problem, the more evident the resemblance. A few typical examples include communications networks [55], machine learning [16], multimedia processing [80], and image processing [51].…”

Section: Computational Considerations Of Modularitymentioning

confidence: 99%

Modularity in neural computing

1999

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Section: Computational Considerations Of Modularitymentioning

confidence: 99%

Modularity in neural computing

1999

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“…Due to the rapid development of a new paradigm of intelligent systems and methodology called computational intelligence (including fuzzy logic, neural networks and evolutionary computing), many novel algorithms and applications of computational intelligence in telecommunications systems including various aspects of ATM networks (such as connection admission control, policing, and routing) have been proposed and developed [e.g., [11]- [15]]. However, in the recently published works on direct traffic prediction using various techniques including fuzzy logic (e.g., [16]), neural networks (e.g., [5], [17]- [19]), and adaptive linear models (e.g., [20]- [22]), studies were only conducted on one-frame-ahead traffic prediction.…”

Section: Introductionmentioning

confidence: 99%

Real-Time VBR Video Traffic Prediction for Dynamic Bandwidth Allocation

Liang

2004

IEEE Trans. Syst., Man, Cybern. C

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In this paper, we systematically investigate the longterm, online, real-time variable-bit-rate (VBR) video traffic prediction, which is the key and complicated component for advanced predictive dynamic bandwidth control and allocation framework for the future networks and Internet multimedia services. We focus on neural network-based approach for traffic prediction and demonstrate that the prediction performance and robustness of neural network predictors can be significantly improved through multiresolution learning. We show that neural network traffic predictor trained through the multiresolution learning (called multiresolution learning NN traffic predictor) can successfully predict various real-world VBR video traffic up to hundreds of frames in advance, which then lays a solid foundation for predictive dynamic bandwidth control and allocation mechanism. Also, dynamic bandwidth control/allocation based on long-term traffic prediction is discussed in detail.Index Terms-Dynamic bandwidth allocation, multiresolution learning, network traffic prediction, neural networks, variablebit-rate (VBR) video traffic.

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“…Recently, use of Artificial Neural Networks (ANN) in the traffic management of ATM networks has gained momentum [4][5][6][7]15]. An adaptive controller using neural networks for congestion control in ATM multiplexers has been developed [9, l4].…”

Section: Introductionmentioning

confidence: 99%

ATM congestion control using Minimal Resource Allocation Networks (MRAN)

Soon

Sundararajan

Saratchandran

2003

Neural Computing & Applications

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This paper presents a congestion control scheme for ATM traffic using a minimal radial basis function neural network referred to as Minimal Resource Allocation Network (MRAN). Earlier studies have shown that MRAN is well suited for online adaptive control of nonlinear time varying systems as it can adjust its size by adding and pruning the hidden neurons based on the input data. Since ATM traffic is nonlinear and time varying performance of MRAN as a congestion controller is investigated here. These studies are carried out using OPNET to model the ATM traffic. The ATM traffic model consists of bursty, Variable BitRate (VBR) and custom traffic in a multiplexed form so as to generate a heavily congested traffic situation. For this scenario, the controller has to minimize the congestion episodes and maintain the Quality of Service (QoS) requirements. This paper compares the performance of the MRAN congestion controller with that of a modified Explicit Rate Indication with Congestion Avoidance (ERICA) scheme and a Back-Propagation (BP) neural controller. Simulation results indicate that MRAN controller performs better than the modified ERICA and BP controller in reducing the congestion episodes and maintaining the desirable QoS.

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Applications of neurocomputing in traffic management of ATM networks

Cited by 25 publications

References 25 publications

Modularity in neural computing

Modularity in neural computing

Real-Time VBR Video Traffic Prediction for Dynamic Bandwidth Allocation

ATM congestion control using Minimal Resource Allocation Networks (MRAN)

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