2023/01/27 by Dongsheng Yang, Yang, Dongsheng, Daniel S. Berger +5 · 2 citations
Computer Science · #Advanced Data Storage Technologies #Caching and Content Delivery #Data Stream Mining Techniques #Distributed #FOS: Computer and information sciences #Operating Systems (cs.OS) #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2301.11886
openalex publication_date 2023/01/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.