Probabilistic Assignment of Movies to Storage Devices in a Video-On-Demand System

A video-on-demand server must satisfy a large customer base and a diverse archive of movies under changing movie popularity and daily load peaks. These requirements must be satisfied under the constraints imposed by storage device costs, capacities, I/O bandwidths, and geographic locations. In this paper we describe a partitioning of video data (movies) onto a video-on-demand storage hierarchy to achieve efficient storage and I/O bandwidth utilization. Our approach uses a probabilistic model of movie popularity in data distribution and replication to balance user requests with available disk I/O bandwidth. The results can be applied in the design of a distributed video-on-demand system.