Multiple-source network tomography

Abstract

Assessing and predicting internal network performance is of fundamental importance in problems ranging from routing optimization to anomaly detection. The problem of estimating internal network structure and link-level performance from end-to-end measurements is called network tomography. This thesis investigates the general network tomography problem involving multiple sources and receivers, building on existing single source techniques. Using multiple sources potentially provides a more accurate and refined characterization of the internal network. The general network tomography problem is decomposed into a set of smaller components, each involving just two sources and two receivers. A novel measurement procedure is proposed which utilizes a packet arrival order metric to classify two-source, two-receiver topologies according to their associated model-order. Then a decision-theoretic framework is developed, enabling the joint characterization of topology and internal performance. A statistical test is designed which provides a quantification of the tradeoff between network topology complexity and network performance estimation.