The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
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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Cites
The Art of Computer Programming, Volume 3, Sorting and Searching
Wilkes, M. V. (1974) · The Computer Journal · doi:10.1093/comjnl/17.4.324 · Cited by 707
The working set model for program behavior
Denning, P. J. (1968) · Communications of the ACM · doi:10.1145/363095.363141 · Cited by 662
Operating system support for database management
Stonebraker, M. (1981) · Communications of the ACM · doi:10.1145/358699.358703 · Cited by 382
Data caching issues in an information retrieval system
Alonso, R. D. H., Barbará, D., & García-Molina, H. (1990) · ACM Transactions on Database Systems · doi:10.1145/88636.87848 · Cited by 319
Principles of Optimal Page Replacement
Aho, A. V., Denning, P. J., & Ullman, J. D. (1971) · Journal of the ACM · doi:10.1145/321623.321632 · Cited by 289
Buffer management in relational database systems
Sacco, G. M., & Schkolnick, M. (1986) · ACM Transactions on Database Systems · doi:10.1145/7239.7336 · Cited by 143
Gray, J., & Putzolu, F. (1987) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/38713.38755 · Cited by 137
An approximate analysis of the LRU and FIFO buffer replacement schemes
Dan, A., & Towsley, D. (1990) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/98460.98525 · Cited by 127
Data cache management using frequency-based replacement
Robinson, J. T., & Devarakonda, M. V. (1990) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/98460.98523 · Cited by 70
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Jin, H., Lee, S., Shin, H., & Kim, J. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642349 · Cited by 99
Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge
Zobaed, S., Mokhtari, A., Champati, J. P., Kourouma, M., & Salehi, M. A. (2022) · 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc56403.2022.00012 · Cited by 15
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Metadata
- Authors
- O'Neil, E. J., O'Neil, P. E., & Weikum, G.
- Venue
- ACM SIGMOD Record
- Published
- 1993-06
- Volume
- 22
- Issue
- 2
- Pages
- 297-306
- ISSN
- 0163-5808 (ISSN-L)
- OpenAlex
- W4241687679
- Cited by
- 369
- References
- 9
- FWCI
- 3.72
- Type
- journal article
- Language
- en
- Open access
- closed
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- O'Neil, E. J., O'Neil, P. E., & Weikum, G. (1993). The LRU-K page replacement algorithm for database disk buffering. ACM SIGMOD Record. https://doi.org/10.1145/170036.170081
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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