Publications in Scientific Journals:
M. Laner, P. Svoboda, M. Rupp:
"Parsimonious Network Traffic Modeling By Transformed ARMA Models";
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.
Traf c modeling, transformed Gaussian, ARMA model, parsimoniousness
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Electronic version of the publication:
Project Head Philipp Svoboda:
LOLA internal doktoral project
Created from the Publication Database of the Vienna University of Technology.