Fitting a Model to Data in Loss Tomography

TL;DR

This paper examines the limitations of existing loss tomography estimators, highlights the importance of data-model consistency, and proposes new estimators tailored to different data classes and a universal estimator.

Contribution

It identifies the misconception about maximum likelihood estimators in loss tomography, classifies data sets, and introduces new estimators suited for each class and a general one for all data types.

Findings

Existing estimators are only valid for specific data classes.

A classification of data sets improves estimator validity.

A new universal estimator applicable to all data classes is proposed.

Abstract

Loss tomography has received considerable attention in recent years and a number of estimators have been proposed. Although most of the estimators claim to be the maximum likelihood estimators, the claim is only partially true since the maximum likelihood estimate can be obtained at most for a class of data sets. Unfortunately, few people are aware of this restriction that leads to a misconception that an estimator is applicable to all data sets as far as it returns a unique solution. To correct this, we in this paper point out the risk of this misconception and illustrate the inconsistency between data and model in the most influential estimators. To ensure the model used in estimation consistent with the data collected from an experiment, the data sets used in estimation are divided into 4 classes according to the characteristics of observations. Based on the classification, the validity of an estimator is defined and the validity of the most influential estimators is evaluated. In addition, a number of estimators are proposed, one for a class of data sets that have been overlooked. Further, a general estimator is proposed that is applicable to all data classes. The discussion starts from the tree topology and end at the general topology.