Load management in distributed video servers

We define and formulate various policies for load management in distributed video servers. We propose a predictive placement policy that determines the degree of replication necessary for popular videos using a cost based optimization procedure based on a priori predictions of expected subscriber requests. For scheduling requests, we propose an adaptive scheduling policy that compares the relative utilization of resources in a video server to determine an assignment of requests to replicas. To optimize storage utilization, we also devise methods for dereplication of videos based on changes in their popularities and in server usage patterns. Performance evaluations indicate that a load management procedure which uses a judicious combination of the different policies performs best for most server configurations. Advances in storage technologies are making high performance video servers a reality. These video servers are being deployed over emerging broadband networks to deliver a variety of interactive, digital video services to thousands of residential subscribers. To meet the scalability requirements in such large deployments, distributed video server architectures are being considered (M. Buddhikot and G. Parulkar, 1995). We propose various methods for load management that are targeted at improving the cost effectiveness of distributed video servers