A Network Coding Approach to Loss Tomography

TL;DR

This paper introduces a network coding-based framework for loss tomography that enhances link loss rate estimation, improves identifiability, and reduces complexity in both tree and general network topologies.

Contribution

It presents a novel approach leveraging network coding to improve loss tomography, addressing challenges in complex network structures.

Findings

Enhanced link loss rate identifiability

Improved estimation accuracy and bandwidth efficiency

Reduced complexity in probe path selection

Abstract

Network tomography aims at inferring internal network characteristics based on measurements at the edge of the network. In loss tomography, in particular, the characteristic of interest is the loss rate of individual links and multicast and/or unicast end-to-end probes are typically used. Independently, recent advances in network coding have shown that there are advantages from allowing intermediate nodes to process and combine, in addition to just forward, packets. In this paper, we study the problem of loss tomography in networks with network coding capabilities. We design a framework for estimating link loss rates, which leverages network coding capabilities, and we show that it improves several aspects of tomography including the identifiability of links, the trade-off between estimation accuracy and bandwidth efficiency, and the complexity of probe path selection. We discuss the cases of inferring link loss rates in a tree topology and in a general topology. In the latter case, the benefits of our approach are even more pronounced compared to standard techniques, but we also face novel challenges, such as dealing with cycles and multiple paths between sources and receivers. Overall, this work makes the connection between active network tomography and network coding.