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M. Laner, P. Svoboda, M. Rupp:
"Modeling Randomness in Network Traffic";
Poster: SIGMETRICS/Performance'12, London, UK; 06-11-2012 - 06-15-2012; in: "Proceedings of the SIGMETRICS/Performance'12", (2012), 2 pages.

A continuous challenge in the field of network traffic modeling
is to map recorded traffic onto parameters of random
processes, in order to enable simulations of the respective
traffic. A key element thereof is a convenient model which
is simple, yet, captures the most relevant statistics.
This work aims to find such a model which, more precisely,
enables the generation of multiple random processes
with arbitrary but jointly characterized distributions, auto-
correlation functions and cross-correlations. Hence, we
present the definition of a novel class of models, the derivation
of a respective closed-form analytical representation and
its application on real network traffic.
Our modeling approach comprises: (i) generating statistical
dependent Gaussian random processes, (ii) introducing
auto-correlation to each process with a linear filter and, (iii)
transforming them sample-wise by real-valued polynomial
functions in order to shape their distributions. This particular
structure allows to split the parameter fitting problem
into three independent parts, each of which solvable by standard
methods. Therefore, it is simple and straightforward
to fit the model to measurement data.

traffic modeling, time series, distribution, autocorrelation, cross-correlation, online gaming

http://publik.tuwien.ac.at/files/PubDat_208382.pdf