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Video Management and Resource Allocation for a Large-Scale VoD Cloud

We consider providing large-scale Netflix-like video-on-demand (VoD) service on a cloud platform, where cloud proxy servers are placed close to user pools. Videos may have heterogeneous popularity at different geo-locations. A repository provides video backup for the network, and the proxy servers collaboratively store and stream videos. To deploy the VoD cloud, the content provider rents resources consisting of link capacities among servers, server storage, and server processing capacity to handle remote requests. We study how to minimize the deployment cost by jointly optimizing video management (in terms of video placement and retrieval at servers) and resource allocation (in terms of link, storage, and processing capacities), subject to a certain user delay requirement on video access. We first formulate the joint optimization problem and show that it is NP-hard. To address it, we propose Resource allocation And Video management Optimization (RAVO), a novel and efficient algorithm based on linear programming with proven optimality gap. For a large video pool, we propose a video clustering algorithm to substantially reduce the run-time computational complexity without compromising performance. Using extensive simulation and trace-driven real data, we show that RAVO achieves close-to-optimal performance, outperforming other advanced schemes significantly (often by multiple times).