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The LRU-K page replacement algorithm for database disk buffering
Abstract
This paper introduces a new approach to database disk buffering, called the LRU-K method. The basic idea of LRU-K is to keep track of the times of the last K references to popular database pages, using this information to statistically estimate the interarrival times of references on a page by page basis. Although the LRU-K approach performs optimal statistical inference under relatively standard assumptions, it is fairly simple and incurs little bookkeeping overhead. As we demonstrate with simulation experiments, the LRU-K algorithm surpasses conventional buffering algorithms in discriminating between frequently and infrequently referenced pages. In fact, LRU-K can approach the behavior of buffering algorithms in which page sets with known access frequencies are manually assigned to different buffer pools of specifically tuned sizes. Unlike such customized buffering algorithms however, the LRU-K method is self-tuning, and does not rely on external hints about workload characteristics. Furthermore, the LRU-K algorithm adapts in real time to changing patterns of access.
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Cache replacement algorithms like LRU-K, ARC, and Belady's optimal algorithm directly inform our adaptive compaction strategies.
“Cache replacement algorithms like LRU-K [14], ARC [15], and Belady's optimal algorithm [17] directly inform our adaptive compaction strategies.”
From AgentRM: An OS-Inspired Resource Manager for LLM Agent Systems (2026) · cites this paper as [14]
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AgentRM: An OS-Inspired Resource Manager for LLM Agent Systems
She, J. (2026) · arXiv · doi:10.48550/arxiv.2603.13110
“Cache replacement algorithms like LRU-K [14], ARC [15], and Belady’s optimal algorithm [17] directly inform our adaptive compaction strategies.”
Randomization Boosts KV Caching, Learning Balances Query Load: A Joint Perspective
Wu, F., Silwal, S., Qiuyi, & Zhang (2026) · arXiv · doi:10.48550/arxiv.2601.18999
“Although the traditional Least Recently Used (LRU) eviction policy (O’Neil et al., 1993; Fiat et al., 1991) remains the dominant choice in current LLM-serving system designs (Zheng et al., 2024; Kwon et al., 2023; NVIDIA, 2025), its strategy of evicting the oldest tokens within specific…”
Metadata
- Authors
- O'Neil, E. J., O'Neil, P. E., & Weikum, G.
- Affiliations
- University of Massachusetts Boston (US)
- ETH Zurich (CH)
- Venue
- Proceedings of the 1993 ACM SIGMOD international conference on Management of data
- Publisher
- ACM
- Published
- 1993-06
- Pages
- 297-306
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2054308357
- Cited by
- 892
- References
- 0
- Citations by year
- 2026: 15 · 2025: 10 · 2024: 13 · 2023: 16 · 2022: 14 · 2021: 23 · 2020: 31 · 2019: 34 · 2018: 43 · 2017: 43 · 2016: 34 · 2015: 33 · 2014: 34 · 2013: 31 · 2012: 40
- FWCI
- 13.09
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGMOD International Conference on Management of Data (SIGMOD) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Data Storage Technologies
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Keywords
- page replacement algorithms, buffer pool, self-tuning, workload adaptation, statistical inference
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