Temporal Locality in Today's Content Caching: Why it Matters and How to Model it

TL;DR

This paper introduces the Shot Noise Model (SNM), a new traffic model that accurately captures temporal locality in content request patterns, improving cache performance predictions based on real YouTube traffic data.

Contribution

The paper presents the SNM, a simple yet effective model for realistic content request traffic, validated with real data, enhancing cache system analysis and design.

Findings

SNM outperforms existing models in capturing temporal locality.

Real YouTube traffic exhibits specific characteristics critical for accurate cache modeling.

SNM enables scalable and precise cache performance evaluations.

Abstract

The dimensioning of caching systems represents a difficult task in the design of infrastructures for content distribution in the current Internet. This paper addresses the problem of defining a realistic arrival process for the content requests generated by users, due its critical importance for both analytical and simulative evaluations of the performance of caching systems. First, with the aid of YouTube traces collected inside operational residential networks, we identify the characteristics of real traffic that need to be considered or can be safely neglected in order to accurately predict the performance of a cache. Second, we propose a new parsimonious traffic model, named the Shot Noise Model (SNM), that enables users to natively capture the dynamics of content popularity, whilst still being sufficiently simple to be employed effectively for both analytical and scalable simulative studies of caching systems. Finally, our results show that the SNM presents a much better solution to account for the temporal locality observed in real traffic compared to existing approaches.