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A Neural-Based Technique for Estimating Self-Similar Traffic Average Queueing Delay


Estimating buffer latency is one of the most important challenges in the analysis and design of traffic control algorithms. In this paper a novel approach for estimating average queueing delay in multiple source queueing systems is introduced. The approach relies on the modeling power of neural networks in predicting self-similar traffic patterns in order to determine the arrival rate and the packet latency of low loss, moderately loaded queueing systems accommodating such traffic patterns.

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