Network traffic arises from the superposition of Origin-Destination (OD) flows. Hence, a thorough understanding of OD flows is essential for modeling network traffic, and for addressing a wide variety of problems including traffic engineering, traffic matrix estimation, capacity planning, forecasting and anomaly detection. However, to date, OD flows have not been closely studied, and there is very little known about their properties. We present the first analysis of complete sets of OD flow timeseries, taken from two different backbone networks (Abilene and Sprint-Europe). Using Principal Component Analysis (PCA), we find that the set of OD flows has small intrinsic dimension. In fact, even in a network with over a hundred OD flows, these flows can be accurately modeled in time using a small number (10 or less) of independent components or dimensions. We also show how to use PCA to systematically decompose the structure of OD flow timeseries into three main constituents: common periodic trends, short-lived bursts, and noise. We provide insight into how the various constitutents contribute to the overall structure of OD flows and explore the extent to which this decomposition varies over time.
The empirical bases for the application of PCA to the anomaly detection field are provided in REF , where the authors perform an analysis of several traffic measurements taken over two backbone networks (Abilene and Geant) highlighting, by means of PCA, that these measurements have a small intrinsic dimension.
Structural analysis of network traffic flows
SIGMETRICS '04/Performance '04
Computer Science