Unravelling the Impact of Temporal and Geographical Locality in Content Caching Systems

TL;DR

This paper introduces the Shot Noise Model (SNM), a simple traffic model capturing temporal and geographical locality in user content requests, enabling better analysis of caching system performance.

Contribution

The paper presents the SNM, a new traffic model that accurately represents real request patterns and facilitates analytical and scalable simulation studies of caching policies.

Findings

SNM effectively captures real traffic locality characteristics.

Analytical results show different cache performance impacts compared to IRM.

The model enables scalable analysis of caching systems.

Abstract

To assess the performance of caching systems, the definition of a proper process describing the content requests generated by users is required. Starting from the analysis of traces of YouTube video requests collected inside operational networks, we identify the characteristics of real traffic that need to be represented and those that instead can be safely neglected. Based on our observations, we introduce a simple, parsimonious traffic model, named Shot Noise Model (SNM), that allows us to capture temporal and geographical locality of content popularity. The SNM is sufficiently simple to be effectively employed in both analytical and scalable simulative studies of caching systems. We demonstrate this by analytically characterizing the performance of the LRU caching policy under the SNM, for both a single cache and a network of caches. With respect to the standard Independent Reference Model (IRM), some paradigmatic shifts, concerning the impact of various traffic characteristics on cache performance, clearly emerge from our results.