Dynamic Adaptive Mapping of Videos in a Hierarchical TV-Anytime Server Network

In this paper, we propose a hierarchical architecture of server 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 server 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.

Dynamic Adaptive Mapping of Videos in a Hierarchical TV-Anytime Server Network

In this paper, we propose a hierarchical architecture of server network for supporting a largescale 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 server network. Based on the predicted access pattern and importance functions of videos in the next time frame, the predictive assignment is intended to address long-term variations in the system. Furthermore, we present an adaptive reallocation policy to handle the short-term variations. This adaptive reallocation is based on revenue/cost of different videos. Its objective is to maximize the overall revenue that the server system generates. 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 aslo show tha..