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

A Learned Cache Eviction Framework with Minimal Overhead