2026/05/31 by Zejia Qi
Computer Science · #cs.OS #cs.LG
6 pages, 8 figures, 3 listings. Policies and harnesses: https://github.com/JayAndJef/cache_ext_lc . Model and visualizations: https://github.com/JayAndJef/learnedcache
arxiv created 2026/07/30 · arxiv updated 2026/08/03
Any device that runs Linux uses the Linux page cache, a central pillar in OS and application performance, serving to reduce extraneous disk access. Many page cache eviction policies have been developed but remain bound by the rigidity of heuristics. Promising research has been done on neural cache eviction policies, but only in the field of user-space applications such as CDNs. We present LearnedCache, a set of machine-learning-based page cache eviction policies that run live inside the Linux kernel through the cacheext eBPF framework. LearnedCache trains a lightweight perceptron per workload on kernel trace data collected using eBPF to predict whether eviction candidates will be reused within a bounded horizon and deploys it through two eBPF policies. The resulting policies consistently compete with and outperform both external heuristic policies and the kernel's own, improving application throughput by up to 44% versus standard kernel policies.