Using data network metrics, graphics, and topology to explore network characteristics

TL;DR

This paper applies Vardi's network metrics to real data, demonstrating how these metrics reveal traffic characteristics and help analyze network behavior by focusing on specific network segments.

Contribution

It adapts and applies Vardi's network metrics to real-world data, illustrating their utility in understanding network traffic and characteristics.

Findings

Metrics reveal traffic patterns and network features.

Conditioning on network segments uncovers specific traffic behaviors.

Application demonstrates practical utility of network metrics.

Abstract

Yehuda Vardi introduced the term network tomography and was the first to propose and study how statistical inverse methods could be adapted to attack important network problems (Vardi, 1996). More recently, in one of his final papers, Vardi proposed notions of metrics on networks to define and measure distances between a network's links, its paths, and also between different networks (Vardi, 2004). In this paper, we apply Vardi's general approach for network metrics to a real data network by using data obtained from special data network tools and testing procedures presented here. We illustrate how the metrics help explicate interesting features of the traffic characteristics on the network. We also adapt the metrics in order to condition on traffic passing through a portion of the network, such as a router or pair of routers, and show further how this approach helps to discover and explain interesting network characteristics.