Ordinal Rating of Network Performance and Inference by Matrix Completion

TL;DR

This paper introduces a scalable method for network performance measurement using ordinal ratings and matrix completion, reducing costs and enabling accurate inference without structural network data.

Contribution

It presents a novel approach combining ordinal rating and matrix completion for network performance assessment, inspired by recommender systems.

Findings

Regularized matrix factorization yields accurate performance inference.

The approach reduces measurement costs and simplifies metric processing.

Scalable inference does not require network structural information.

Abstract

This paper addresses the large-scale acquisition of end-to-end network performance. We made two distinct contributions: ordinal rating of network performance and inference by matrix completion. The former reduces measurement costs and unifies various metrics which eases their processing in applications. The latter enables scalable and accurate inference with no requirement of structural information of the network nor geometric constraints. By combining both, the acquisition problem bears strong similarities to recommender systems. This paper investigates the applicability of various matrix factorization models used in recommender systems. We found that the simple regularized matrix factorization is not only practical but also produces accurate results that are beneficial for peer selection.