Strategic selection and replication of movies by trend-calibrated movie-demand model

To cope with the fluctuation in movie popularity, video-on-demand systems need to maintain an optimal set of movies. Replication of the most popular movies across multiple storage devices is also needed to increase the bandwidth of these movies in meeting their high demand. However, selection and replication of movies can only be varied within the constraints imposed by the online storage space and organization. We evaluate three movie-demand models based on which strategic decisions on selection and replication can be exercised. The long-term static movie-demand (SMD) model is a quantized version of a trivial approach. The bounded-SMD (B-SMD) model is an improved version of SMD with bounded movie lifespan. We propose a new model called the trend-calibrated movie-demand (TCMD) model. Through simulation studies we have found that TCMD can significantly improve the percentage of customer requests satisfied