Dynamic adaptive mapping of videos in a hierarchical TV-Anytime server network

In this paper we propose a hierarchical architecture of sewer network for supporting a large-scale TV-Anytime system. We present and formulate various policies used for the effective mapping of videos in the runtime of the TV-Anytime system. A predictive assignment policy is presented to periodically determine the mapping of videos to the sewer network. Furthermore, we present an adaptive reallocation policy to handle the short-term variations. Our simulation results demonstrate that these policies yield an efficient system and make the system more adaptive to changes in videos popularity and system state. Our simulation results also show that batching and multicasting significantly improves the system throughput during the high load periods and makes the system more scalable.