The design and evaluation of the Simple Self-Similar Sequences Generator
Information Sciences: an International Journal
Generating LRD traffic traces using bootstrapping
ITC20'07 Proceedings of the 20th international teletraffic conference on Managing traffic performance in converged networks
Fast synthesis of persistent fractional Brownian motion
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Practical modelling for generating self-similar VBR video traffic
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Generation of self-similar processes for simulation studies of telecommunication networks
Mathematical and Computer Modelling: An International Journal
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It is generally accepted that self-similar (or fractal) processes may provide better models of teletraffic in modern computer networks than Poisson processes. 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 pseudo-random self-similar sequences, based on the fractional Gaussian noise (FGN) and a wavelet transform, is proposed and analyzed 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 O(n).