Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Cooperating leaky bucket for average rate enforcement of VBR video traffic in ATM networks
INFOCOM '95 Proceedings of the Fourteenth Annual Joint Conference of the IEEE Computer and Communication Societies (Vol. 3)-Volume - Volume 3
Real-time VBR video traffic prediction for dynamic bandwidth allocation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Rate regulation with feedback controller in ATM networks-a neural network approach
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
A comparative study of the statistical methods suitable for network traffic estimation
ICCOM Proceedings of the 13th WSEAS international conference on Communications
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This work presents a novel feedback rate regulator using the multiple leaky bucket (MLB) for variable bit rate (VBR) self-similar traffic that is based on the traffic load prediction by time-delayed neural networks in ATM networks. In the MLB mechanism, the leak rate and buffer capacity of each leaky bucket (LB) can be dynamically adjusted based on the buffer occupancy. A finite-duration impulse response (FIR) multilayer neural network is used to predict the incoming traffic load and pass the information to the feedback rate regulator. Ten real world MPEG1 and ten synthesized traffic traces are used to validate the performance of the MLB and the MLB with an FIR prediction mechanism. Simulation results demonstrate that the cell loss rate using MLB and MLB with an FIR filter-based predictor can be significantly reduced compare to the conventional leaky bucket method.