Statistical characterization of a real video on demand service: User behaviour and streaming-media workload analysis

The video on demand service characterized in this article has surpassed 4 years of service, and the access log database stores information of more than 160,000 reproductions and 900 videos. The wide variety of subjects, the range of content lengths and a frequent update of contents (several new videos every day), clearly differentiate this study from other previous research limited to specific users, subjects or environments. We present a statistical study of user behaviour and streaming traffic, analyzing session characteristics, mistaken reproductions, amount of media delivered, number and length of pauses and jumps in the reproduction, popularity and daily access profile. The results of the analysis will allow us to develop simulation models and workload generators to evaluate different scenarios and situations of the service.