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

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.Comment: 7 pages, 7 figures, Accepted for publication in ACM Computer Communication Revie

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

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.Comment: 14 pages, 11 Figures, 2 Appendice

Experimental Paradigm for the Assessment of the Non-pharmacological Mechanism of Action in Medical Device Classification: The Example of Glycerine as Laxative

The evolution of medical devices has led to the introduction of medical devices that include “substances” and which, due to their presentation and sites of application may resemble medicinal products. The difference between substance-based medical devices and medicinal products lies in the proper definition of the principal mechanism of action. The major problem at the moment is the lack of a proper procedure for the demonstration of a mechanism that is “not pharmacological, immunological or metabolic.” We aimed to design an experimental set up to demonstrate the difference between the mechanism of action of two substances used commonly for the treatment of constipation, lubiprostone (example of medicinal product) and glycerine (example of medical device). By implementing cellular models and molecular analyses we demonstrate the difference in their mechanism of action. This set up can be considered an example on the possibility to define a paradigm for the case by case study of the mechanism of action of substances and combination of substances in medical devices

Efficient analysis of caching strategies under dynamic content popularity

In this paper we develop a novel technique to analyze both isolated and interconnected caches operating under different caching strategies and realistic traffic conditions. The main strength of our approach is the ability to consider dynamic contents which are constantly added into the system catalogue, and whose popularity evolves over time according to desired profiles. We do so while preserving the simplicity and computational efficiency of models developed under stationary popularity conditions, which are needed to analyze several caching strategies. Our main achievement is to show that the impact of content popularity dynamics on cache performance can be effectively captured into an analytical model based on a fixed content catalogue (i.e., a catalogue whose size and objects' popularity do not change over time).Comment: to appear at Infocom 201