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arXiv · 1501.03354

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

Stefano Traverso·Mohamed Ahmed·Michele Garetto·Paolo Giaccone·Emilio Leonardi·Saverio Niccolini
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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.

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Stefano Traverso, Mohamed Ahmed, Michele Garetto, Paolo Giaccone, Emilio Leonardi, Saverio Niccolini. 2015-01-14. Unravelling the Impact of Temporal and Geographical Locality in Content Caching Systems. https://arxiv.org/abs/1501.03354

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