Research Article
An Inverse Problem Approach for Content Popularity Estimation
@ARTICLE{10.4108/eai.14-12-2015.2262621, author={Felipe Olmos and Bruno Kauffmann}, title={An Inverse Problem Approach for Content Popularity Estimation}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={3}, number={9}, publisher={ACM}, journal_a={SIS}, year={2016}, month={1}, keywords={popularity distribution, mixture model, maximum likelihood estimation, performance models, caching}, doi={10.4108/eai.14-12-2015.2262621} }
- Felipe Olmos
Bruno Kauffmann
Year: 2016
An Inverse Problem Approach for Content Popularity Estimation
SIS
EAI
DOI: 10.4108/eai.14-12-2015.2262621
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 taken due to 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.
Copyright © 2015 F. Olmos and B. Kauffmann, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.