Trace-Driven Analysis of ICN Caching Algorithms on Video-on-Demand Workloads.

Even though a key driver for Information-Centric Networking (ICN) has been the rise in Internet video traffic, there has been surprisingly little work on analyzing the interplay between ICN and video ? which ICN caching strategies work well on video work- loads and how ICN helps improve video-centric quality of experience (QoE). In this work, we bridge this disconnect with a trace- driven study using 196M video requests from over 16M users on a country-wide topology with 80K routers. We evaluate a broad space of content replacement (e.g., LRU, LFU, FIFO) and content placement (e.g., leave a copy everywhere, probabilistic) strategies over a range of cache sizes. We highlight four key findings: (1) the best placement and re- placement strategies depend on the cache size and vary across improvement metrics; that said, LFU+probabilistic caching [37] is a close-to-optimal strategy overall; (2) video workloads show considerable caching-related benefits (e.g., -- 10% traffic reduction) only with very large cache sizes (≥ 100GB); (3) the improvement in video QoE is low (≥ 12%) if the content provider already has a substantial geographical presence; and (4) caches in the middle and the edge of the network, requests from highly populated regions and without content servers, and requests for popular content contribute most to the overall ICN-induced improvements in video QoE.