Network Tomography Based on Additive Metrics

TL;DR

This paper introduces a phylogenetic-inspired framework for network tomography, enabling efficient, robust, and provably correct inference of network topology and link performance from measurements.

Contribution

It develops polynomial-time distance-based algorithms with proven correctness, consistency, robustness, and optimality properties for network inference tasks.

Findings

Algorithms are consistent with increasing data

Algorithms are robust to measurement errors

Algorithms achieve optimal $l_inity$-radius

Abstract

Inference of the network structure (e.g., routing topology) and dynamics (e.g., link performance) is an essential component in many network design and management tasks. In this paper we propose a new, general framework for analyzing and designing routing topology and link performance inference algorithms using ideas and tools from phylogenetic inference in evolutionary biology. The framework is applicable to a variety of measurement techniques. Based on the framework we introduce and develop several polynomial-time distance-based inference algorithms with provable performance. We provide sufficient conditions for the correctness of the algorithms. We show that the algorithms are consistent (return correct topology and link performance with an increasing sample size) and robust (can tolerate a certain level of measurement errors). In addition, we establish certain optimality properties of the algorithms (i.e., they achieve the optimal $l_\infty$-radius) and demonstrate their effectiveness via model simulation.