Video-on-demand (VoD) service providers are intensely interested in transport, storage, streaming and caching in content delivery networks. Today’s 5,000-hour library may grow toward the 750,000-hour “Long Tail ” movie and TVseries catalog. We propose a method to calculate how much of a library should be cached. Much previous work focused on theoretical caching concepts, or the dynamics of cache filling and reclamation. Our method explicitly considers the impact of the available video server equipment; we present a VoD design tool comprising a novel cost function, hit ratio estimation and heuristic. 1.

Version 5.2- Last modified on 2005-04-27

Video-on-Demand (VoD) services are very user-friendly, but also complex and resource demanding. Deployments involve careful design of many mechanisms where content attributes and usage models should be taken into account. We define, and propose a methodology to solve, the VoD Equipment Allocation Problem of determining the number and type of streaming servers with directly attached storage (VoD servers) to install at each potential location in a metropolitan area network topology such that deployment costs are minimized. We develop a cost model for VoD deployments based on streaming, storage and transport costs and train a parametric function that maps the amount of available storage to a worst-case hit ratio. We observe the impact of having to determine the amount of storage and streaming co-jointly, and determine the minimum demand required to deploy replicas as well as the average hit ratio at each location. We observe that common video-on-demand server configurations lead to the installation of excessive storage, because a relatively high hit-ratio can be achieved with small amounts of storage so streaming requirements dominate.