On Netflix catalog dynamics and caching performance

Multimedia streaming applications have substantially changed the market policy of an increasing number of content providers that offer streaming services to the users. The need for effective video content delivery re-fueled interest for caching: since the Web-like workload of the 90s are not longer fit to describe the new Web of videos, in this work we investigate the suitability of the publicly available Netflix dataset for caching studies. Our analysis shows that, as the dataset continuously evolves (i) a steady state description is not statistically meaningful and (ii) despite the cache hit ratio decreases due to the growth of active movies in the catalog, simple caching replacement approaches are close to the optimum given the growing skew in the popularity distribution over the time. Additionally, we point out that, since the dataset reports logs of movie ratings, anomalies arise when ratings are considered to be movie views. At the same time, we show anomalies yield conservative caching results, that reinforces the soundness of our study.