Design, performance analysis, and implementation of a super-scalar video-on-demand system

Despite the availability of video-on-demand (VoD) services in a number of cities around the world, large-scale deployment of VoD services in a metropolitan area is still economically impractical. This study presents a novel super-scalar architecture for building very large-scale and efficient VoD systems. The proposed architecture combines the use of static multicast, dynamic multicast, and intelligent client-side caching to vastly reduce server and network resource requirement. Moreover, in sharp contrast to conventional VoD systems where the system cost increases at least linearly with the system scale, the proposed architecture becomes more efficient as the system scales up and can ultimately be scaled up to serve any number of users while still keeping the startup latency short. This paper presents this new architecture, proposes methods to support interactive playback controls without the need for additional server or client resources, and derives an approximate performance model to relate the startup latency with other system parameters. The performance model is validated using simulation and the architecture is evaluated under various system settings. Lastly, a system implementation is presented and benchmarking results obtained to further verify the architecture, the performance model, and the simulation results.