[Zurück]


Zeitschriftenartikel:

M. Laner, P. Svoboda, M. Rupp:
"Parsimonious Network Traffic Modeling By Transformed ARMA Models";
IEEE Access, 2 (2014), S. 40 - 55.



Kurzfassung englisch:
Generating synthetic data traffic which statistically resembles its recorded counterpart is one of the main goals of network traffic modeling. Equivalently, one or several random processes shall be created, exhibiting multiple prescribed statistical measures. In this article we present a framework enabling the joint representation of distributions, auto-correlations and cross-correlations of multiple processes.
This is achieved by so called transformed Gaussian ARMA models. They constitute an analytically tractable framework, which allows for the separation of the fitting problems into sub-problems for individual measures. Accordingly, known fitting techniques and algorithms can be deployed for the respective solution.
The proposed framework exhibits promising properties: (i) relevant statistical properties such as heavy tails and long-range dependencies are manageable, (ii) the resulting models are parsimonious, (iii) the fitting procedure is fully automatic and (iv) the complexity of generating synthetic traffic is very low. We evaluate the framework with traced traffic, i.e., aggregated traffic, online gaming and video streaming. The queueing responses of synthetic and recorded traffic exhibit identical statistics.
This article provides guidance for high quality modeling of network traffic. It proposes a unifying framework, validates several fitting algorithms and suggests combinations of algorithms suited best for specific traffic types.

Schlagworte:
Traf c modeling, transformed Gaussian, ARMA model, parsimoniousness


"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/ACCESS.2013.2297736

Elektronische Version der Publikation:
http://publik.tuwien.ac.at/files/PubDat_227027.pdf



Zugeordnete Projekte:
Projektleitung Philipp Svoboda:
LOLA internal doktoral project


Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.