Dongsheng Yang will present his General Exam on Monday, May 9, 2022 at 1:30pm via Zoom. Zoom link: princeton.zoom.us/j/6605501024 The members of his committee are as follows: Kai Li (adviser), Wyatt Lloyd, Ravi Netravali Abstract: 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 not practical for high-throughput caching systems. We introduce Machine learning At the Tail (MAT), a framework to build efficient ML-based caching systems by integrating with a heuristic algorithm. MAT treats the heuristic algorithm as a “filter” to select 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 experiments show that MAT can reduce the number of ML predictions of the state-of-the-art ML-based cache from 63 to 2, reducing the software overhead by 31 times, while achieving similar miss ratios. Link to Reading List: [ https://docs.google.com/document/d/1WeBtRlmeY9Oulz6nhGeFoAYKPb4IC6tzkWmKX0gr... | https://docs.google.com/document/d/1WeBtRlmeY9Oulz6nhGeFoAYKPb4IC6tzkWmKX0gr... ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.