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 sur-prisingly 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 experi-ence (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 im-provement metrics; that said, LFU+probabilistic caching [37] is a close-to-optimal strategy overall; (2) video workloads show con-siderable 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 re-gions and without content servers, and requests for popular content contribute most to the overall ICN-induced improvements in video QoE