Active Topology Inference using Network Coding

TL;DR

This paper introduces a novel method for inferring network topology by leveraging network coding, which creates topology-dependent correlations in receiver observations, enabling more accurate reconstruction of complex network structures.

Contribution

The paper presents new algorithms for topology inference using network coding, including hierarchical clustering for trees and a decomposition-merge approach for DAGs, improving accuracy over prior methods.

Findings

Algorithms accurately infer network topologies in simulations.

Network coding enhances the distinguishability of network components.

Compared to non-coding methods, our approach shows improved accuracy.

Abstract

Our goal is to infer the topology of a network when (i) we can send probes between sources and receivers at the edge of the network and (ii) intermediate nodes can perform simple network coding operations, i.e., additions. Our key intuition is that network coding introduces topology-dependent correlation in the observations at the receivers, which can be exploited to infer the topology. For undirected tree topologies, we design hierarchical clustering algorithms, building on our prior work. For directed acyclic graphs (DAGs), first we decompose the topology into a number of two-source, two-receiver (2-by-2) subnetwork components and then we merge these components to reconstruct the topology. Our approach for DAGs builds on prior work on tomography, and improves upon it by employing network coding to accurately distinguish among all different 2-by-2 components. We evaluate our algorithms through simulation of a number of realistic topologies and compare them to active tomographic techniques without network coding. We also make connections between our approach and alternatives, including passive inference, traceroute, and packet marking.