An Inverse Problem Approach for Content Popularity Estimation

TL;DR

This paper presents an inverse problem approach using maximum likelihood estimation to accurately infer content popularity from request traffic data, improving content performance modeling in information-centric networks.

Contribution

It introduces a novel inverse problem formulation for content popularity estimation and demonstrates its effectiveness with real and synthetic data.

Findings

Inverse problem formulation improves popularity estimation accuracy

Maximum likelihood estimation effectively solves the inverse problem

Accurate popularity inference enhances content performance models

Abstract

The Internet increasingly focuses on content, as exemplified by the now popular Information Centric Networking paradigm. This means, in particular, that estimating content popularities becomes essential to manage and distribute content pieces efficiently. In this paper, we show how to properly estimate content popularities from a traffic trace. Specifically, we consider the problem of the popularity inference in order to tune content-level performance models, e.g. caching models. In this context, special care must be brought on the fact that an observer measures only the flow of requests, which differs from the model parameters, though both quantities are related by the model assumptions. Current studies, however, ignore this difference and use the observed data as model parameters. In this paper, we highlight the inverse problem that consists in determining parameters so that the flow of requests is properly predicted by the model. We then show how such an inverse problem can be solved using Maximum Likelihood Estimation. Based on two large traces from the Orange network and two synthetic datasets, we eventually quantify the importance of this inversion step for the performance evaluation accuracy.