发表机构
Harvard University（哈佛大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出SR-Gadgets，一种无需改变驱逐启发式或队列结构即可使现有缓存算法抗扫描的增强组件，在5,538条生产轨迹上平均降低缺失率最高23.1%，并减少悬崖和Belady异常。
AI 中文摘要
块缓存通常服务于扫描密集型I/O工作负载，这促使对抗扫描驱逐算法的广泛研究。许多此类算法采用多队列结构。然而，它们主要关注一次性扫描，不能很好地处理重复扫描。重复扫描带来的两个重要挑战是缺失率悬崖（miss-ratio cliffs），即缓存大小的微小增加会急剧降低缺失率，以及Belady异常（Belady's anomalies），即增加缓存大小反而增加缺失率。在本文中，我们首先开发了两个定量指标来衡量这些行为。利用这些指标，我们发现LIRS是唯一一种抗扫描（几乎无悬崖和异常）的多队列算法。与常规认知相反，我们表明栈距离（stack distance）并不是使LIRS抗扫描的秘密武器。相反，调节队列是其抗扫描的关键。基于这些见解，我们设计了Gadgets（小工具），这是一种易于集成的增强组件，可以在不改变现有算法驱逐启发式或队列结构的情况下使其抗扫描。我们在五种算法中实现了Gadgets：S3-FIFO、SIEVE、ARC、2Q和TinyLFU，并使它们抗扫描。在5,538条生产轨迹上评估，所有增强算法都优于其基础版本，将缺失率降低了最多23.1%，同时在生产轨迹上持续减少了悬崖和Belady异常。
英文摘要
Block caches commonly serve scan-heavy I/O workloads, motivating extensive studies on scan-resistant eviction algorithms. Many of these algorithms adopt a multi-queue structure. However, they focus primarily on one-time scans and do not handle repeated scans well. Two important challenges from repeated scans are miss-ratio cliffs, where a small increase in cache size sharply reduces the miss ratio, and Belady's anomalies, where increasing the cache size increases the miss ratio. In this paper, we first develop two quantitative metrics to measure these behaviors. With these metrics, we find that LIRS is the only multi-queue algorithm that is scan-resistant (almost cliff- and anomaly-free). Contrary to conventional wisdom, we show that stack distance is not the secret sauce that makes LIRS scan-resistant. Instead, regulating the queues are the key to its scan resistance. Based on these insights, we design the \gadgetprefix Gadgets, easy-to-integrate augmentations that make existing algorithms scan-resistant without changing their eviction heuristics or queue structures. We implement the \gadgetprefix Gadgets in five algorithms: S3-FIFO, SIEVE, ARC, 2Q, and TinyLFU, and make them scan-resistant. Evaluated on 5,538 production traces, all augmented algorithms outperform their base versions, reducing miss ratios by up to 23.1% while consistently reducing cliffs and Belady's anomalies across the production traces.
