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 more than 10 times! 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 analytically show how HiFi approaches the performance of an optimal caching policy and how to tune HiFi to come as close to optimal performance as the traffic conditions allow. 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 Beladys MIN algorithm under typical operator conditions.