Abstract
The article describes P/M/1/K queuing model and Hurst parameter estimation unit with Wavelet Transform for real-time estimation of the traffic self-similarity parameter in Simulink. It consists of simulation results, which show the possibility to estimate Hurst parameter in real-time by means of Wavelet transform. It is offered to iteratively calculate the averaged Hurst parameter estimation, which improves the accuracy to the accuracy of the estimation over entire traffic series and in some cases even higher, as it has been shown in simulation results analysis. The estimation deviations have been analyzed as well as variance of such deviations. The results also give directions for further research to improve accuracy of traffic parameters estimation. DOI: http://dx.doi.org/10.5755/j01.eee.19.3.3702
Highlights
It is widely common for network traffic to be correlated in long-time scale, i.e. the traffic is self-similar
The self-similar traffic with same utilization level and buffer overflow probability would require memory capacity of K = 5 · 108 packets estimated by formula from [3]–[8], for Hurst parameter of H = 0.9 in order to provide efficient service
During the modelling process the Hurst parameter evaluates are saved to MATLAB workspace, which allows taking all benefits from Simulink integration into MATLAB
Summary
It is widely common for network traffic to be correlated in long-time scale, i.e. the traffic is self-similar (fractal). There are other examples mentioned in [2], such as video-traffic with variable bit-rate, Wide Area Networks, etc Such long-term correlation significantly impacts queue length, and as of such – waiting delays for data packets. According to [7], one should calculate average Hurst parameter over time instead of momentary values This addition to the model has been made and wavelet-estimator outputs both these values in time. That due to self-similar traffic nature, the estimation of selfsimilarity parameter can’t be realized in situations when there is no data about entity count (a “pause”). In such cases instead of calculation according to (4) the previous averaged value remains the same. This method is supposed to provide the highest estimation accuracy and will be used to compare the iteratively averaged value at the end of the entity count series
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