Measurement-based real-time traffic model classification

A new method for real-time traffic model classification is proposed and evaluated. The method classifies the current measured traffic to a "best-fit" model selected from a library of candidate models using statistical estimation techniques. A two-model system has been prototyped and evaluated through simulation experiments. The experimental system consists of a short-range dependent model and long-range dependent model, and uses the estimated Hurst parameter to select between the two models to choose the model requiring an equivalent bandwidth (EB) that is closest to the actual required EB. Results have demonstrated that the two-model system can classify the observed traffic to the correct model with fair accuracy, and can automatically detect a change in traffic characteristics after a delay. The design parameters affecting the classification accuracy and delay to detect traffic changes are discussed.