Catalog dynamics: Impact of content publishing and perishing on the performance of a LRU cache

Abstract

The Internet heavily relies on Content Distribution Networks and transparent caches to cope with the ever-increasing traffic demand of users. Content, however, is essentially versatile: once published at a given time, its popularity vanishes over time. All requests for a given document are then concentrated between the publishing time and an effective perishing time.In this paper, we propose a new model for the arrival of content requests, which takes into account the dynamical nature of the content catalog. Based on two large traffic traces collected on the Orange network, we use the semi-experimental method and determine invariants of the content request process. This allows us to define a simple mathematical model for content requests; by extending the so-called "Che approximation", we then compute the performance of a LRU cache fed with such a request process, expressed by its hit ratio. We numerically validate the good accuracy of our model by comparison to tracebased simulation.

An Inverse Problem Approach for Content Popularity Estimation

“…A key point in our approach is that we consider the probability that a document receives a given number of requests, rather than the probability that a request is directed to a given document. This representation is consistent with recently developed caching models [17,21,6]. Moreover, it allows us to avoid the fitting of a rank-frequency plot, which is in essence an order statistic and exhibits over-fitting.…”

“…#vod) set. More details on the collection and processing of these two datasets can be found in [17].…”

“…In particular, our framework can be extended to to renewal [6,1] and cluster processes [17,21]. In these cases new challenges arises, due the reformulation of the ML method.…”

“…In these cases new challenges arises, due the reformulation of the ML method. For example, the randomized parameter in other traffic models is not univariate, but multivariate [17] or even an stochastic process [6]. Another factor to consider is time censure, due the greater impact of the time variable in stochastic models other than IRM-M.…”

“…By the conditioning argument of Proposition 3 in [17], it can be shown that the expectation of the number of hits HC = H δK satisfies the identity…”