Feedback control of congestion in packet switching networks: the case of a single congested node
IEEE/ACM Transactions on Networking (TON)
Design of a fuzzy traffic controller for ATM networks
IEEE/ACM Transactions on Networking (TON)
Congestion control and traffic management in ATM networks: recent advances and a survey
Computer Networks and ISDN Systems
Robust implicit self-tuning regulator: convergence and stability
Automatica (Journal of IFAC)
Adaptive algorithms for feedback-based flow control in high-speed, wide-area ATM networks
IEEE Journal on Selected Areas in Communications
ATM communications network control by neural networks
IEEE Transactions on Neural Networks
The rate-based flow control framework for the available bit rate ATM service
IEEE Network: The Magazine of Global Internetworking
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This paper proposes a neural network (NN)-based adaptive control methodology to prevent congestion in high-speed asynchronous transfer mode (ATM) networks. The buffer dynamics at the switch is modeled as a nonlinear discrete-time system and a NN-based predictive controller is designed to predict the explicit values of the transmission rates of the sources so as to prevent congestion. Tuning methods are provided for the NN weights to estimate the unpredictable and statistically fluctuating network traffic. Mathematical analysis is given to demonstrate the stability of the closed-loop system so that a desired quality of service (QoS) can be guaranteed. The QoS is defined in terms of cell loss ratio (CLR) and latency. We derive design rules mathematically for selecting the NN tuning algorithm such that the desired performance is guaranteed during congestion and potential tradeoffs are shown. Simulation results are provided to justify the theoretical conclusions for single source/single switch scenario using ON/OFF data. Finally, comparison studies are also included to show the effectiveness of the proposed method over conventional rate-based and thresholding techniques during simulated congestion.