Loss Rate Inference in Multi-Sources and Multicast-Based General Topology

TL;DR

This paper develops a new analytical framework for loss rate inference in general network topologies with multiple sources and multicast, providing scalable maximum likelihood estimators and strategies for link loss estimation.

Contribution

It introduces a novel set of polynomial-based expressions and closed-form solutions for maximum likelihood estimation in complex topologies, advancing beyond prior tree-focused methods.

Findings

Derived polynomial expressions for probe correlation in general topologies.

Proposed closed-form MLE solutions for path pass rates.

Link-based estimator shown to be more general and optimal.

Abstract

Loss tomography has received considerable attention in recent years and a number of estimators have been proposed. Unfortunately, almost all of them are devoted to the tree topology despite the general topology is more common in practice. In addition, most of the works presented in the literature rely on iterative approximation to search for the maximum of a likelihood function formed from observations, which have been known neither scalable nor efficient. In contrast to the tree topology, there is few paper dedicated to the general topology because of the lack of understanding the impacts created by the probes sent by different sources. We in this paper present the analytical results obtained recently for the general topology that show the correlation created by the probes sent by multiple sources to a node located in an intersection of multiple trees. The correlation is expressed by a set of polynomials of the pass rates of the paths connecting the sources to the node. In addition to the expression, a closed form solution is proposed to obtain the MLE of the pass rates of the paths connecting the sources to the node. Then, two strategies are proposed to estimate the loss rate of a link for the general topology: one is path-based and the other is link-based, depending on whether we need to obtain the pass rate of a path first. The two strategies are compared in the context of the general topology that shows each has its advantages and the link-based one is more general. Apart from proving the estimates obtained are the MLEs, we prove the estimator presented here has the optimal asymptotic property.