A Content-aware Data-plane for Efficient and Scalable Video Delivery

Internet users consume increasing quantities of video content with higher Quality of Experience (QoE) expectations. Network scalability thus becomes a critical problem for video delivery as traditional Content Delivery Networks (CDN) struggle to cope with the demand. In particular, content-awareness has been touted as a tool for scaling CDNs through clever request and content placement. Building on that insight, we propose a network paradigm that provides application-awareness in the network layer, enabling the offload of CDN decisions to the data-plane. Namely, it uses chunk-level identifiers encoded into IPv6 addresses. These identifiers are used to perform network-layer cache admission by estimating the popularity of requests with a Least-Recently-Used (LRU) filter. Popular requests are then served from the edge cache, while unpopular requests are directly redirected to the origin server, circumventing the HTTP proxy. The parameters of the filter are optimized through analytical modeling and validated via both simulation and experimentation with a testbed featuring real cache servers. It yields improvements in QoE while decreasing the hardware requirements on the edge cache. Specifically, for a typical content distribution, our evaluation shows a 22% increase of the hit rate, a 36% decrease of the chunk download-time, and a 37% decrease of the cache server CPU load.