  Recent work shows the effectiveness of Machine Learning (ML) to reduce cache
miss ratios by making better eviction decisions than heuristics. However,
state-of-the-art ML caches require many predictions to make an eviction
decision, making them impractical for high-throughput caching systems. This
paper introduces Machine learning At the Tail (MAT), a framework to build
efficient ML-based caching systems by integrating an ML module with a
traditional cache system based on a heuristic algorithm. MAT treats the
heuristic algorithm as a filter to receive high-quality samples to train an ML
model and likely candidate objects for evictions. We evaluate MAT on 8
production workloads, spanning storage, in-memory caching, and CDNs. The
simulation experiments show MAT reduces the number of costly ML
predictions-per-eviction from 63 to 2, while achieving comparable miss ratios
to the state-of-the-art ML cache system. We compare a MAT prototype system with
an LRU-based caching system in the same setting and show that they achieve
similar request rates.
