ABR traffic management using minimal resource allocation (neural) networks

Dimirovski, Georgi M. (Dogus Author)For the Asynchronous Transfer Mode (ATM) networks with time-varying multiple time-delays, a more realistic model for the available bit rate (ABR) traffic class with explicit rate feedback is introduced. A fuzzy-immune controller is designed, which can adjust the rates of ABR on-line, overcome the bad effect caused by the saturation nonlinearity and satisfy the weighted fairness. Also, the sufficient condition that guarantees the stability of the closed-loop system with a fuzzy-immune controller is presented in theory for the first time. The algorithm exhibits good performance, and most importantly, has a solid theoretical foundation and can be implemented in practice easily. Simulation results show that the control system is rapid, adaptive, robust, and meanwhile, the quality of service (QoS) is guaranteed

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Error tolerance based support vector machine for regression

Chen

Huang

et al. 2011

Neurocomputing

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ABR traffic management using minimal resource allocation (neural) networks

Cited by 6 publications

References 7 publications

Bandwidth Prediction and Congestion Control for ABR Traffic Based on Neural Networks

Bandwidth Prediction and Congestion Control for ABR Traffic Based on Neural Networks

Performance and robustness analysis of a fuzzy-immune flow controller in ATM networks with time-varying multiple time-delays

Error tolerance based support vector machine for regression

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