Robustifying Learning-Augmented Caching Efficiently without Compromising 1-Consistency

The online caching problem aims to minimize cache misses when serving a sequence of requests under a limited cache size. While naive learning-augmented caching algorithms achieve ideal $1$-consistency, they lack robustness guarantees. Existing robustification methods either sacrifice $1$-consistency or introduce excessive computational overhead. In this paper, we introduce Guard, a lightweight robustification framework that enhances the robustness of a broad class of learning-augmented caching algorithms to $2H_{k-1} + 2$, while preserving their $1$-consistency. Guard achieves the current best-known trade-off between consistency and robustness, with only $\mathcal{O}(1)$ additional per-request overhead, thereby maintaining the original time complexity of the base algorithm. Extensive experiments across multiple real-world datasets and prediction models validate the effectiveness of Guard in practice.

Authors

• Peng Chen
• Hailiang Zhao
• Jiaji Zhang National Key Laboratory for Novel Software Technology, Nanjing University School of Artificial Intelligence, Nanjing University
• Xueyan Tang
• Yixuan Wang Northwestern University
• Shuiguang Deng College of Computer Science and Technology, Zhejiang University, Hangzhou, China

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