发表机构
Université d’Avignon; Linköping University（阿维尼翁大学; 林雪平大学）
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H-MC，一种基于Hedge的LRU与LFU缓存混合策略，在保留Hedge遗憾保证的同时最小化切换成本，解决了LeCar的线性遗憾问题。
AI 中文摘要
缓存系统通常依赖于简单的驱逐策略，如最近最少使用（LRU）和最少频繁使用（LFU），这两种策略在互补的请求模式下表现良好。最近的策略如LeCar和Cacheus利用在线学习中的专家问题思想，结合了LRU和LFU。具体而言，在缓存未命中时，它们根据通过跟踪过去驱逐历史更新的分数得出的概率，在两种驱逐规则之间进行随机化。尽管这些策略表现出强大的经验性能，但仍不清楚它们是否能在每个请求序列上保证渐近地达到LRU和LFU中较优者的性能，即相对于该基准是否实现次线性遗憾。我们首先证明，即使具有无界历史，LeCar在面对不知情对手时也会遭受线性遗憾。然后，我们提出H-MC，一种基于Hedge的虚拟LRU和LFU缓存混合方法，该方法保留Hedge的选择概率及其遗憾保证，同时在所有具有这些边际分布的联合选择规则中最小化切换成本。
英文摘要
Caching systems often rely on simple eviction policies such as Least Recently Used (LRU) and Least Frequently Used (LFU), which perform well in complementary request regimes. Recent policies such as LeCar and Cacheus combine LRU and LFU using ideas from the experts problem in online learning. Specifically, upon a miss, they randomize between the two eviction rules using probabilities derived from scores updated by tracking the history of past evictions. While these policies exhibit strong empirical performance, it remains unclear whether they are guaranteed, on every request sequence, to perform asymptotically as well as the better of LRU and LFU, i.e., whether they achieve sublinear regret with respect to this benchmark. We first show that LeCar suffers linear regret against an oblivious adversary, even with unbounded history. We then propose H-MC, a Hedge-based mixture of virtual LRU and LFU caches that preserves Hedge's selection probabilities, and hence its regret guarantees, while minimizing the switching cost among all joint selection rules with these marginals.
