Joint optimization of content replication and server selection for video-on-demand

We study providing large-scale video-on-demand (VoD) service to distributed users. In order to achieve scalability in user capacity and reduce the load of the core network, local servers with heterogeneous storage are deployed. Each server replicates the movie segments depending on their access probabilities. Considering the realistic scenario that underlay delay is a function of the total traffic in the link (including cross-traffic), we address two important problems to achieve low user interactive delay: 1) Which segments should each server replicate under the constraints of their capacities to achieve network-wide good locality effect? This is the so-called content replication (CR) problem; and 2) Given a number of remote servers with the requested segment, which one should serve the user? This is the so-called server selection (SS) problem. CR and SS problems couple with each other. In this paper, we propose a simple and distributed algorithm which seeks to jointly optimize CR and SS. The algorithm, termed CR-SS, achieves good caching locality by adaptively replacing segments and selecting servers with a simple lookup. Simulation results on Internet-like topologies show that CR-SS outperforms existing and state-of-the-art approaches by a wide margin, achieving substantially lower user delay.