Access flow control scheme for ATM networks using neural-network-based traffic prediction

Fan, Zhong; Mars, P.

doi:10.1049/ip-com:19971408

Cited by 17 publications

(16 citation statements)

References 9 publications

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“…The queue has a constant service rate of cells per unit time. Denoting by the queue length at time , then we have the following Lindley's equation [34], [40] (see Fig. 2) indicating (26) cells are lost if the buffer overflows.…”

Section: Congestion Control Using Traffic Predictionmentioning

confidence: 99%

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Traffic modeling, prediction, and congestion control for high-speed networks: a fuzzy AR approach

Chen

Peng²,

Wang

2000

IEEE Trans. Fuzzy Syst.

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Abstract-In general, high-speed network traffic is a complex, nonlinear, nonstationary process and is significantly affected by immeasurable parameters and variables. Thus, a precise model of this process becomes increasingly difficult as the complexity of the process increases. Recently, fuzzy modeling has been found to be a powerful method to effectively describe a real, complex, and unknown process with nonlinear and time-varying properties. In this study, a fuzzy autoregressive (fuzzy-AR) model is proposed to describe the traffic characteristics of high-speed networks. The fuzzy-AR model approximates a nonlinear time-variant process with a combination of several linear local AR processes using a fuzzy clustering method. We propose that the use of this fuzzy-AR model has greater potential for congestion control of packet network traffic. The parameter estimation problem in fuzzy-AR modeling is treated by a clustering algorithm developed from actual traffic data in high-speed networks. Based on adaptive AR-prediction model and queueing theory, a simple congestion control scheme is proposed to provide an efficient traffic management for high-speed networks. Finally, using the actual ethernet-LAN packet traffic data, several examples are given to demonstrate the validity of this proposed method for high-speed network traffic control.Index Terms-Cell loss rate, fuzzy-AR approach, quality of service (QoS), traffic prediction.

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Section: Congestion Control Using Traffic Predictionmentioning

confidence: 99%

“…The voice source is produced by an interrupted Poisson process (IPP) [40], [41]. A voice source alternates between talk spurts (active) and silent periods.…”

Section: Examplementioning

confidence: 99%

Traffic modeling, prediction, and congestion control for high-speed networks: a fuzzy AR approach

Chen

Peng²,

Wang

2000

IEEE Trans. Fuzzy Syst.

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“…As training continues, the model keeps on adjusting its weights according to the input , and gradually becomes more accurate. Because of an efficient learning mechanism of neural network, it can predict network congestion with good accuracy [20,28]. After prediction, congestion can be controlled by many ways.…”

Section: Neural Networkmentioning

confidence: 99%

“…After prediction, congestion can be controlled by many ways. One way is to throttle the input arrival rate [20,28], or apply different routing mechanisms [38][39][40]. The following subsections explain the congestion prediction done through neural network model and congestion control by throttling the source or applying different routing techniques.…”

Section: Neural Networkmentioning

confidence: 99%

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Prediction and preemptive control of network congestion in distributed real-time environment

Dhanoa¹

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Due to ever increasing demand for network capacity, the congestion problem is inflating. Congestion results in queuing within the network, packet loss and increased delays. It should be controlled to increase the system throughput and quality of service. The existing congestion control approaches such as source throttling and re-routing focus on controlling congestion after it has already happened. However, it is much more desirable to predict future congestion based on the current state and historical data, so that efficient controlling techniques can be applied to prevent congestion from happening in future.We have proposed a Neural Network Prediction-based routing (NNPR) protocol to predict as well as control the network traffic in distributed real time environment.

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Traffic Control in an ATM Network Using Rough Set Theory

Martyna

1999

Lecture Notes in Computer Science

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Access flow control scheme for ATM networks using neural-network-based traffic prediction

Cited by 17 publications

References 9 publications

Traffic modeling, prediction, and congestion control for high-speed networks: a fuzzy AR approach

Traffic modeling, prediction, and congestion control for high-speed networks: a fuzzy AR approach

Prediction and preemptive control of network congestion in distributed real-time environment

Traffic Control in an ATM Network Using Rough Set Theory

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