AI 中文总结
本文提出学习增强型启发式算法（LAH）框架，构建S4-FIFO算法，在提升缓存效率、鲁棒性的同时保持可解释性，性能优于S3-FIFO、3L-Cache等现有算法。
AI 中文摘要
缓存技术在系统栈中被广泛应用以提升性能与效率，缓存淘汰算法是其核心。现有缓存淘汰策略主要分为两大类：静态启发式算法（如2Q、S3-FIFO）和智能算法（如ARC、LRB）。智能缓存可适配不同工作负载，相比静态启发式算法具备更高的效率与鲁棒性潜力，但本文发现现有智能缓存存在目标不匹配和不稳定的问题。本文提出学习增强型启发式算法（LAH）框架，用于学习静态启发式算法的缓存级参数；通过分离数据平面与控制平面，LAH在数据平面支持简单、高速的读写操作，在控制平面利用缓存级特征执行异步学习。本文通过S4-FIFO（一种智能S3-FIFO缓存淘汰算法）验证LAH的有效性：在4140条生产轨迹上预训练单个模型并嵌入S4-FIFO以学习最优缓存参数，在1035条评估轨迹上，S4-FIFO的平均效率较S3-FIFO提升26%，较最优现有算法3L-Cache提升8%；S4-FIFO也具备鲁棒性，在最差轨迹上较FIFO的未命中率仅提升0.8%，而3L-Cache较FIFO的未命中率提升8.8%；此外，S4-FIFO的决策可解释，语言模型可针对特定配置的选择提供依据。
英文摘要
Caching is widely used across the system stack to improve performance and efficiency, with eviction algorithms at its core. Existing cache eviction policies fall into two broad categories: static heuristics (e.g., 2Q, S3-FIFO) and smart algorithms (e.g., ARC, LRB). Smart caches can adapt to workloads and have the potential to achieve higher efficiency and robustness than static heuristics. However, we find that existing smart caches suffer from objective mismatches and instability. We introduce Learning-Augmented Heuristics (LAH), a framework that learns the cache-level parameters of static heuristics. By decoupling the data and control planes, LAH supports simple, high-speed data reads and writes on the data plane, while performing occasional asynchronous learning on the control plane using cache-level features. We demonstrate the effectiveness of LAH through S4-FIFO, a Smart S3-FIFO cache eviction algorithm. We pre-train a single model on 4,140 production traces and embed it in S4-FIFO to learn optimal cache parameters. On 1,035 evaluation traces, S4-FIFO improves the mean efficiency by 26% compared to S3-FIFO and by 8% compared to 3L-Cache, the best state-of-the-art algorithm. S4-FIFO is also robust---increasing miss ratio over FIFO by 0.8% on the worst trace, whereas 3L-Cache increases FIFO's miss ratio by 8.8%. Finally, S4-FIFO's decisions are also interpretable: a language model can provide a rationale for why a particular configuration was chosen.
Comments22 pages, accepted to OSDI '26
