Network Tomography: Estimating Source-Destination Traffic Intensities from Link Data

TLDR

Estimating node‑to‑node traffic intensity from link measurements under Poisson assumptions, for deterministic or Markovian routing, is challenging due to computational difficulties in maximum likelihood estimation. The study proposes a method‑of‑moments approach to estimate source‑destination traffic intensities. The authors derive moment‑based estimators by solving a linear inverse problem with positivity constraints via an EM algorithm and validate the approach with a small simulation study.

Abstract

Abstract The problem of estimating the node-to-node traffic intensity from repeated measurements of traffic on the links of a network is formulated and discussed under Poisson assumptions and two types of traffic-routing regimens: deterministic (a fixed known path between each directed pair of nodes) and Markovian (a random path between each directed pair of nodes, determined according to a known Markov chain fixed for that pair). Maximum likelihood estimation and related approximations are discussed, and computational difficulties are pointed out. A detailed methodology is presented for estimates based on the method of moments. The estimates are derived algorithmically, taking advantage of the fact that the first and second moment equations give rise to a linear inverse problem with positivity restrictions that can be approached by an EM algorithm, resulting in a particularly simple solution to a hard problem. A small simulation study is carried out.