Table of Contents
ISRN Computational Mathematics
Volume 2012 (2012), Article ID 103509, 10 pages
Research Article

Study of Stationary Load Increase of Computer-Network Traffic via Dynamic Principal-Component Analysis

1Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada M5B 2K3
2The Fields Institute for Research in Mathematical Sciences, 222 College Street, Toronto, ON, Canada M5T 3J1
3Department of Mathematics and Statistics, University of Guelph, Guelph, ON, Canada N1G 2W1

Received 24 July 2012; Accepted 16 August 2012

Academic Editors: D. S. Corti, L. S. Heath, and R. Tuzun

Copyright © 2012 Shengkun Xie and Anna T. Lawniczak. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Many network monitoring applications and performance analysis tools are based on the study of an aggregate measure of network traffic, for example, number of packets in transit (NPT). The simulation modeling and analysis of this type of performance indicator enables a theoretical investigation of the underlying complex system through different combination of network setups such as routing algorithms, network source loads or network topologies. To detect stationary increase of network source load, we propose a dynamic principal component analysis (PCA) method, first to extract data features and then to detect a stationary load increase. The proposed detection schemes are based on either the major or the minor principal components of network traffic data. To demonstrate the applications of the proposed method, we first applied them to some synthetic data and then to network traffic data simulated from the packet switching network (PSN) model. The proposed detection schemes, based on dynamic PCA, show enhanced performance in detecting an increase of network load for the simulated network traffic data. These results show usefulness of a new feature extraction method based on dynamic PCA that creates additional feature variables for event detection in a univariate time series.