An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems
Summary: ML-based automatic DBMS knob tuning evaluated with OtterTune on Oracle using real workload traces; compares three ML tuning algorithms. Achieves ~45% gains over enterprise configs; reveals deployment and measurement issues absent in synthetic studies. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Dana Van Aken (Carnegie Mellon University; OtterTune)
- 2. Dongsheng Yang (Princeton University)
- 3. Sebastien Brillard (Société Générale)
- 4. Ari Fiorino (OtterTune)
- 5. Bohan Zhang (Carnegie Mellon University)
- 6. Christian Bilien (OtterTune)
- 7. Andrew Pavlo (Carnegie Mellon University)
BibTeX Citation
@article{aken_vldb21,
title = {{An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems}},
author = {Van Aken, Dana and Yang, Dongsheng and Brillard, Sebastien and Fiorino, Ari and Zhang, Bohan and Bilien, Christian and Pavlo, Andrew},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {7},
pages = {1241--1253},
doi = {10.14778/3450980.3450992},
url = {https://doi.org/10.14778/3450980.3450992},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
Showing 36 of 36 citing papers.
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next