Intelligent traffic control for ATM broadband networks

Tarraf, A.A.; Habib, I.; Saadawi, T.N.

doi:10.1109/35.466223

Cited by 33 publications

(7 citation statements)

References 10 publications

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“…Since the ®rst paper by Hiramatsu [17] was published, several other papers have addressed various aspects of NN-based CAC techniques [4,23,25,32,39]. Hiramatsu [17] proposes a threelayer, fully connected multi-layer perceptron (MLP) trained with back-propagation for CAC.…”

Section: Nn-based Connection Admission Controlmentioning

confidence: 99%

“…Further reviews on NN applications in ATM trac management problems can be found in the work of Douligeris [7], Park and Lee [25] and Tarraf et al [32].…”

Section: Nn-based Connection Admission Controlmentioning

confidence: 99%

“…The use of neural networks (NNs) in trac management [4,7,23,25,32,39] has been gaining wide acceptance lately due to the following advantages of NNs: · ability to adapt to changes in trac conditions via on-line training, · precise models of the network processes are not required, · ability to give reasonable outputs for input patterns previously not observed, · high computational rates due to parallel hardware implementation, and · ability to perform prediction by learning the relationship between current and future values.…”

Section: Introductionmentioning

confidence: 99%

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Modular neural networks for multi-service connection admission control

Soh

Tham

2001

Computer Networks

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Section: Nn-based Connection Admission Controlmentioning

confidence: 99%

“…Further reviews on NN applications in ATM trac management problems can be found in the work of Douligeris [7], Park and Lee [25] and Tarraf et al [32].…”

Section: Nn-based Connection Admission Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modular neural networks for multi-service connection admission control

Soh

Tham

2001

Computer Networks

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“…Resources should be carefully controlled, guaranteeing the needed quality of service (QoS) in terms of cell loss rate (CLR), blocking probability, delay, etc. and maximizing, at the same time, the network operator revenues [1].…”

Section: Introductionmentioning

confidence: 99%

A dynamic bandwidth resource allocation based on neural networks in euroskyway multimedia satellite system

Camarda

Castellano

Piscitelli

et al. 2003

Int J Communication

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SUMMARYAdvanced traffic management based on the dynamic resource assignment allows a broadband satellite system such as euroskyway (ESW) to dynamically assign the resources of connections. The mechanisms of the dynamic assignment exploit variations of burstiness exhibited by real time and non-real time variable bit rate traffic sources to perform an optimized resource redistribution. The efficiency of the dynamic bandwidth allocation capability (DBAC) depends on the accuracy of the traffic source description; inaccurate assessment of the arrival process will cause an overhead and a degraded utilisation of system resources.In this paper a flexible traffic burstiness predictor for dynamic bandwidth resource allocation based on neural network is presented. The approach is able to perform an online estimation of expected resource requests implementing traffic resource assignment by using a sub-symbolic adaptive representation of the traffic source. The achieved results prove that the flexible approach is more effective than the ones based on fixed schemes designed using analytical traffic source description when applied on the satellite terminal ESW system component.

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“…Thus, characterization of traffic sources and network behaviour is difficult, especially if they need to be done in real-time. Neural network approaches which do not require precise models of the network processes have been used with varying degrees of success for traffic management and congestion control [8], [9], [11], [10].…”

Section: Multi-service Connection Admission Controlmentioning

confidence: 99%

Multi-service connection admission control using modular neural networks

Tham

Soh²

Proceedings. IEEE INFOCOM '98, the Conference on Computer Communications. Seventeenth Annual Joint Conference of the IEEE Compu

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Intelligent traffic control for ATM broadband networks

Cited by 33 publications

References 10 publications

Modular neural networks for multi-service connection admission control

Modular neural networks for multi-service connection admission control

A dynamic bandwidth resource allocation based on neural networks in euroskyway multimedia satellite system

Multi-service connection admission control using modular neural networks

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