# A Learned Cache Eviction Framework with Minimal Overhead

arXiv: https://arxiv.org/abs/2301.11886 · HTML: https://mlsanity.com/paper/2301.11886/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2023.

## Built on by (most cited first, 3 of 3)

- [Toward Robust and Efficient ML-Based GPU Caching for Modern Inference](https://mlsanity.com/paper/2509.20979/) (2025)
- [SCION: Size-aware Policy Orchestration for Nonstationary Object Caches (Long Paper Version)](https://mlsanity.com/paper/2605.01055/) (2026)
- [LearnedCache: eBPF-Integrated Perceptron-Based Eviction Policies for the Linux Page Cache](https://mlsanity.com/paper/2605.26168/) (2026)

## Abstract (arXiv)

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.