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http://dx.doi.org/10.3745/KIPSTC.2004.11C.5.621

Fast Self-Similar Network Traffic Generation Based on FGN and Daubechies Wavelets  

Jeong, Hae-Duck
Lee, Jong-Suk
Publication Information
The KIPS Transactions:PartC / v.11C, no.5, 2004 , pp. 621-632 More about this Journal
Abstract
Recent measurement studies of real teletraffic data in modern telecommunication networks have shown that self-similar (or fractal) processes may provide better models of teletraffic in modern telecommunication networks than Poisson processes. If this is not taken into account, it can lead to inaccurate conclusions about performance of telecommunication networks. Thus, an important requirement for conducting simulation studies of telecommunication networks is the ability to generate long synthetic stochastic self-similar sequences. A new generator of pseu-do-random self-similar sequences, based on the fractional Gaussian nois and a wavelet transform, is proposed and analysed in this paper. Specifically, this generator uses Daubechies wavelets. The motivation behind this selection of wavelets is that Daubechies wavelets lead to more accurate results by better matching the self-similar structure of long range dependent processes, than other types of wavelets. The statistical accuracy and time required to produce sequences of a given (long) length are experimentally studied. This generator shows a high level of accuracy of the output data (in the sense of the Hurst parameter) and is fast. Its theoretical algorithmic complexity is 0(n).
Keywords
Self-similar Process; Teletraffic Generator; Fractional Gaussian Noise; Daubechies Wavelet; Complexity; Hurst Parameter;
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