Data Partitioning for Video-on-Demand Services

Streaming video over the Internet has become a popular service that is projected to outstrip demand over most other real-time streaming services. Thus, it is essential to provide scalable server storage and services without which video-on-demand services will not be able to provide good performance in the face of increasing demand. In this paper, we study algorithms, and propose one, for simple partitioning video-on-demand storage to distribute content and workload among servers within a master-slave architecture. We show the effectiveness of the partitioning strategies by conducting experiments on an actual system to quantify trade-offs between the algorithms.