Expert-based on-line learning and prediction in Content Delivery Networks

Abstract

Machine learning techniques can be used to improve the quality of experience for the end users of Content Delivery Networks (CDNs). In a CDN, the most popular video contents are cached near the end-users in order to minimize the contents delivery latency. The idea developed hereafter consists in using prediction techniques to evaluate the future popularity of video contents in order to decide which should cached. We consider various prediction methods, called experts, coming from different fields (e.g. statistics, control theory). To evaluate the accuracy of the experts' popularity predictions, we assess these experts according to three criteria: cumulated loss, maximum instantaneous loss and best ranking. We also show the importance of a decision maker, called forecaster, that predicts the popularity based on the predictions of selection of several experts. The forecaster based on the best K experts outperforms in terms of cumulated loss the individual experts' predictions and those of the forecaster based on only one expert, even if this expert varies over time.