no code implementations • 30 Apr 2021 • Ginno Millán
A qualitative and quantitative extension of the chaotic models used to generate self-similar traffic with long-range dependence (LRD) is presented by means of the formulation of a model that considers the use of piecewise affine one-dimensional maps.
no code implementations • 2 Mar 2021 • Ginno Millán, Román Osorio-Comparán, Gastón Lefranc
The methodology consists in designing an experiment using estimators that are applied to time series addresses resulting from the capture of high-speed network traffic, followed by addressing the minimum amount of point required to obtain in accurate estimates of the Hurst exponent.
no code implementations • 11 Mar 2010 • Ginno Millán, Gastón Lefranc
It proposes a method of fitting model to a given traffic trace.
no code implementations • 11 Mar 2010 • Ginno Millán
The process existence is presented in term of a new algorithmic that is a variant of the maximum likelihood estimator (MLE) of Whittle, for the calculation of the Hurst exponent (H) of self-similar stationary second order time series of the flows of the individual sources and their aggregation.
no code implementations • 9 Mar 2010 • Ginno Millán, Gastón Lefranc
In the context of the simulations carried out using a simplified multifractal model that is proposed to give an explanation to the locality phenomenon that appears in the estimation of the Hurst exponent in the second-order stationary series that represent the self-similar traffic flows in high-speed computer networks, its formulation is perfected to reduce the variability in the singularity limits and it is demonstrated through by its wavelet variant that this modification leads to a higher resolution in the interval of interest under study.
no code implementations • 5 Mar 2010 • Ginno Millán
This paper studies and analyses the behavior of the Long-Range Dependence in network traffic after classifying traffic flows in aggregated time series.
no code implementations • 5 Mar 2010 • Ginno Millán
The methodology consists in designing an experiment using estimators that are applied to time series, followed by addressing the minimum amount of points required to obtain accurate estimates of the Hurst exponent in real-time.