Title :
Neural network-based ATM QoS estimation
Author :
Sheng, Wenbo ; Rueda, Jose ; Blight, David
Author_Institution :
TRlabs, Winnipeg, Man., Canada
Abstract :
A key technology for the B-ISDN is asynchronous transfer mode (ATM). ATM traffic management or congestion control is needed to guarantee the quality of service (QoS) parameters. A neural network-based QoS estimation is presented to enhance the performance in ATM management so that the service-providers can offer better services to their users. Several methods such as learning vector quantization (LVQ) and self-organizing map (SM) are used to estimate the QoS levels
Keywords :
B-ISDN; asynchronous transfer mode; learning (artificial intelligence); self-organising feature maps; telecommunication computing; telecommunication congestion control; telecommunication network management; telecommunication traffic; vector quantisation; ATM QoS estimation; ATM traffic management; B-ISDN; LVQ; asynchronous transfer mode; congestion control; learning vector quantization; neural network; performance; quality of service; self-organizing map; service providers; supervised learning; training data; Asynchronous transfer mode; B-ISDN; Communication system traffic control; Delay; ISDN; Neural networks; Quality management; Quality of service; Switches; Traffic control;
Conference_Titel :
WESCANEX 97: Communications, Power and Computing. Conference Proceedings., IEEE
Conference_Location :
Winnipeg, Man.
Print_ISBN :
0-7803-4147-3
DOI :
10.1109/WESCAN.1997.627103
