用神经网络优化内核页缓存淘汰，性能提升最高达44%。
LearnedCache: eBPF-Integrated Perceptron-Based Eviction Policies for the Linux Page Cache
- 基于eBPF在内核中部署轻量感知机，实时预测页面是否会被重用。
- 通过内核追踪数据训练模型，在多个工作负载下吞吐提升最高44%。
- 适合追求极致性能的系统级开发者与高负载服务器场景。
所有运行Linux的设备都依赖页缓存这一核心组件，用于减少不必要的磁盘访问。尽管已有多种缓存淘汰策略，但多受限于静态启发式规则。虽有研究尝试将神经网络应用于缓存淘汰，但仅限于用户空间应用（如CDN）。本文提出LearnedCache，一套基于机器学习的页缓存淘汰策略，通过cache_ext eBPF框架在内核中实时运行。该方法利用eBPF采集内核跟踪数据，为每个工作负载训练一个轻量级感知机模型，预测淘汰候选页面在有限时间窗口内是否会被重用，并通过两个eBPF策略部署。实验表明，该策略持续优于外部启发式策略及内核默认策略，应用吞吐最高提升44%。
原文摘要 · Abstract (English)
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 cache_ext 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.
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