IEICE TRANSACTIONS on Communications
Application of Neural Network in ATM Call Admission Control Based on Cell Transfer State Monitoring with Dynamic Threshold
Summary :
Calls using different media which require different transfer quality will arrive at ATM networks. Therefore it is important to develop a method for allocating network resources efficiently to individual calls by judging admission of calls. Various call admission control schemes have been already proposed, and these schemes assume that users specify values of traffic descriptors when they originate calls. However, it is sometimes difficult for users to specify these values accurately. This paper proposes a new ATM call admission control scheme based on cell transfer state monitoring which does not require that users specify values of traffic descriptors in detail when they originate calls. In this proposed scheme, the acceptance or rejection of calls is judged by comparing the monitored cell transfer state value with a threshold prepared in advance. This threshold must be adjusted according to changes in the characteristics of traffic applied to ATM networks. This is one of the most serious problems in the control scheme based on the monitoring of cell transfer state. Herein, this paper proposes neural network application to the control scheme in order to resolve this problem and improve performance. In principle, the threshold can be adjusted automatically by the self-learning function of the neural network, and the control can be maintained appropriately even if the characteristics of traffic applied to ATM networks change drastically. In this paper, the effectiveness of the application of a neural network is clarified by showing the configuration of this proposed control scheme with the neural network, a method for deciding various parameter values needed to implement this control scheme, and finally the results of a performance evaluation of the control scheme. Inputs required by the neural network are also discussed.
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- IEICE TRANSACTIONS on Communications Vol.E78-B No.4 pp.465-475
- Publication Date
- 1995/04/25
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- Type of Manuscript
- Special Section PAPER (Special Issue on Traffic and Quality Control for Communication Networks)
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Nagao OGINO, Yasushi WAKAHARA, "Application of Neural Network in ATM Call Admission Control Based on Cell Transfer State Monitoring with Dynamic Threshold" in IEICE TRANSACTIONS on Communications,
vol. E78-B, no. 4, pp. 465-475, April 1995, doi: .
Abstract: Calls using different media which require different transfer quality will arrive at ATM networks. Therefore it is important to develop a method for allocating network resources efficiently to individual calls by judging admission of calls. Various call admission control schemes have been already proposed, and these schemes assume that users specify values of traffic descriptors when they originate calls. However, it is sometimes difficult for users to specify these values accurately. This paper proposes a new ATM call admission control scheme based on cell transfer state monitoring which does not require that users specify values of traffic descriptors in detail when they originate calls. In this proposed scheme, the acceptance or rejection of calls is judged by comparing the monitored cell transfer state value with a threshold prepared in advance. This threshold must be adjusted according to changes in the characteristics of traffic applied to ATM networks. This is one of the most serious problems in the control scheme based on the monitoring of cell transfer state. Herein, this paper proposes neural network application to the control scheme in order to resolve this problem and improve performance. In principle, the threshold can be adjusted automatically by the self-learning function of the neural network, and the control can be maintained appropriately even if the characteristics of traffic applied to ATM networks change drastically. In this paper, the effectiveness of the application of a neural network is clarified by showing the configuration of this proposed control scheme with the neural network, a method for deciding various parameter values needed to implement this control scheme, and finally the results of a performance evaluation of the control scheme. Inputs required by the neural network are also discussed.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e78-b_4_465/_p
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@ARTICLE{e78-b_4_465,
author={Nagao OGINO, Yasushi WAKAHARA, },
journal={IEICE TRANSACTIONS on Communications},
title={Application of Neural Network in ATM Call Admission Control Based on Cell Transfer State Monitoring with Dynamic Threshold},
year={1995},
volume={E78-B},
number={4},
pages={465-475},
abstract={Calls using different media which require different transfer quality will arrive at ATM networks. Therefore it is important to develop a method for allocating network resources efficiently to individual calls by judging admission of calls. Various call admission control schemes have been already proposed, and these schemes assume that users specify values of traffic descriptors when they originate calls. However, it is sometimes difficult for users to specify these values accurately. This paper proposes a new ATM call admission control scheme based on cell transfer state monitoring which does not require that users specify values of traffic descriptors in detail when they originate calls. In this proposed scheme, the acceptance or rejection of calls is judged by comparing the monitored cell transfer state value with a threshold prepared in advance. This threshold must be adjusted according to changes in the characteristics of traffic applied to ATM networks. This is one of the most serious problems in the control scheme based on the monitoring of cell transfer state. Herein, this paper proposes neural network application to the control scheme in order to resolve this problem and improve performance. In principle, the threshold can be adjusted automatically by the self-learning function of the neural network, and the control can be maintained appropriately even if the characteristics of traffic applied to ATM networks change drastically. In this paper, the effectiveness of the application of a neural network is clarified by showing the configuration of this proposed control scheme with the neural network, a method for deciding various parameter values needed to implement this control scheme, and finally the results of a performance evaluation of the control scheme. Inputs required by the neural network are also discussed.},
keywords={},
doi={},
ISSN={},
month={April},}
Copy
TY - JOUR
TI - Application of Neural Network in ATM Call Admission Control Based on Cell Transfer State Monitoring with Dynamic Threshold
T2 - IEICE TRANSACTIONS on Communications
SP - 465
EP - 475
AU - Nagao OGINO
AU - Yasushi WAKAHARA
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E78-B
IS - 4
JA - IEICE TRANSACTIONS on Communications
Y1 - April 1995
AB - Calls using different media which require different transfer quality will arrive at ATM networks. Therefore it is important to develop a method for allocating network resources efficiently to individual calls by judging admission of calls. Various call admission control schemes have been already proposed, and these schemes assume that users specify values of traffic descriptors when they originate calls. However, it is sometimes difficult for users to specify these values accurately. This paper proposes a new ATM call admission control scheme based on cell transfer state monitoring which does not require that users specify values of traffic descriptors in detail when they originate calls. In this proposed scheme, the acceptance or rejection of calls is judged by comparing the monitored cell transfer state value with a threshold prepared in advance. This threshold must be adjusted according to changes in the characteristics of traffic applied to ATM networks. This is one of the most serious problems in the control scheme based on the monitoring of cell transfer state. Herein, this paper proposes neural network application to the control scheme in order to resolve this problem and improve performance. In principle, the threshold can be adjusted automatically by the self-learning function of the neural network, and the control can be maintained appropriately even if the characteristics of traffic applied to ATM networks change drastically. In this paper, the effectiveness of the application of a neural network is clarified by showing the configuration of this proposed control scheme with the neural network, a method for deciding various parameter values needed to implement this control scheme, and finally the results of a performance evaluation of the control scheme. Inputs required by the neural network are also discussed.
ER -
English
