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Foundations of Automated Database Tuning

Summary: Foundational framework for automated DB tuning using queuing theory and combinatorial optimization to model self-manageability in DBMS. A resource-aware evaluation approach to compare technologies, reveal implicit assumptions, and connect manageability to memory/index usage, with design implications and prior-work survey. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3732
Venue
SIGMOD
Year
2005
Pagerank
4.2612967e-05
Overall Rank
9,893 | 31.18%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
6,398 Endure: A Robust Tuning Paradigm for LSM Trees Under Workload Uncertainty 2022 VLDB 5.0819209e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
6,433 Self-Managing Technology in Database Management Systems 2004 VLDB 5.0642403e-05
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