Hifi: A Hierarchical Filtering Algorithm For Caching Of Online Video

Online video presents new challenges to traditional caching with over a thousand fold increase in number of assets, rapidly changing popularity of assets and much higher throughput requirements.We propose a new hierarchical filtering algorithm for caching online video-HiFi. Our algorithm is designed to optimize hit rate, replacement rate and cache throughput. It has an associated implementation complexity comparable to that of LRU.Our results show that under typical operator conditions, HiFi can increase edge cache byte hit-rate by 5-24% over an LRU policy, but more importantly can increase RAM or memory byte hit-rate by 80% to 200% and reduce replacement rate by 90%! These two factors combined can dramatically increase throughput for most caches. If SSDs are used for storage, the much lower replacement rate may also allow substitution of lower cost MLC based SSDs instead of SLC based SSDs.We extend previous multi-tier analytical models for LRU caches to caches with filtering. We develop a realistic simulation environment for online video using statistics from operator traces. We show that HiFi performs within a few percentage points from the optimal solution which was simulated by Belady's MIN algorithm under typical operator conditions.