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Towards Dynamic and Safe Configuration Tuning for Cloud Databases
Summary: OnlineTune tunes online cloud DBs under dynamic workloads with contextual BO and context-space partition. Safety via black-box/white-box checks and subspace adaptation; results show large gains and far fewer unsafe configurations.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6485
- Venue
- SIGMOD
- Year
- 2022
- Pagerank
- 5.8826802e-05
- Overall Rank
- 4,842 | 66.32%
- DOI
-
10.1145/3514221.3526176
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 22 of 22 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 3,114 |
GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization |
2024 |
VLDB |
7.5451724e-05 |
| 3,812 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
6.7373184e-05 |
| 4,868 |
DBPA: A Benchmark for Transactional Database Performance Anomalies |
2023 |
SIGMOD |
5.8629636e-05 |
| 6,151 |
An Efficient Transfer Learning Based Configuration Adviser for Database Tuning |
2024 |
VLDB |
5.183652e-05 |
| 6,379 |
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning |
2023 |
SIGMOD |
5.0909479e-05 |
| 6,871 |
Towards General and Efficient Online Tuning for Spark |
2023 |
VLDB |
4.8997004e-05 |
| 8,186 |
E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model |
2025 |
VLDB |
4.5651684e-05 |
| 8,617 |
A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning |
2024 |
VLDB |
4.4846425e-05 |
| 8,854 |
Optimizing the cloud? Don't train models. Build oracles! |
2024 |
CIDR |
4.4349047e-05 |
| 9,733 |
ContTune: Continuous Tuning by Conservative Bayesian Optimization for Distributed Stream Data Processing Systems |
2023 |
VLDB |
4.2942813e-05 |
| 9,956 |
SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression |
2025 |
VLDB |
4.2373024e-05 |
| 10,047 |
AgentTune: An Agent-Based Large Language Model Framework for Database Knob Tuning |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,093 |
MCTuner: Spatial Decomposition-Enhanced Database Tuning via LLM-Guided Exploration |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,164 |
ESTune: Bayesian Uncertainty-Guided Early Stopping for Database Configuration Tuning |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,247 |
Why Database Manuals Are Not Enough: Efficient and Reliable Configuration Tuning for DBMSs via Code-Driven LLM Agents |
2026 |
VLDB |
4.1945683e-05 |
| 10,328 |
Libra: One-Shot Parameter Sensitivity Estimation for Transfer Learning in Database Performance Prediction |
2026 |
VLDB |
4.1945683e-05 |
| 10,370 |
Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptions |
2025 |
SIGMOD |
4.1945683e-05 |
| 10,414 |
Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment |
2025 |
SIGMOD |
4.1945683e-05 |
| 10,518 |
High-Throughput Ingestion for Video Warehouse: Comprehensive Configuration and Effective Exploration |
2025 |
SIGMOD |
4.1945683e-05 |
| 10,560 |
A Systematic Study on Early Stopping Metrics in HPO and the Implications of Uncertainty |
2025 |
VLDB |
4.1945683e-05 |
| 10,633 |
AQETuner: Reliable Query-level Configuration Tuning for Analytical Query Engines |
2025 |
VLDB |
4.1945683e-05 |
| 10,859 |
Graph Transformers for Query Plan Representation: Potentials and Challenges |
2025 |
VLDB |
4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 25 of 25 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059038975 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036721403 |
| 204 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034784455 |
| 333 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027206884 |
| 340 |
OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases |
2014 |
VLDB |
0.00026841628 |
| 424 |
Tuning Database Configuration Parameters with iTuned |
2009 |
VLDB |
0.00023616398 |
| 514 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.0002124895 |
| 608 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019235898 |
| 716 |
Query-based Workload Forecasting for Self-Driving Database Management Systems |
2018 |
SIGMOD |
0.00017723171 |
| 758 |
Deep Unsupervised Cardinality Estimation |
2020 |
VLDB |
0.0001706608 |
| 765 |
Automatic Performance Diagnosis and Tuning in Oracle |
2005 |
CIDR |
0.00017016449 |
| 782 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00016729063 |
| 806 |
An End-to-End Learning-based Cost Estimator |
2020 |
VLDB |
0.00016434274 |
| 846 |
Self-tuning Database Technology and Information Services: from Wishful Thinking to Viable Engineering |
2002 |
VLDB |
0.00015997985 |
| 910 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423056 |
| 1,501 |
P-Store: An Elastic Database System with Predictive Provisioning |
2018 |
SIGMOD |
0.00011664869 |
| 1,827 |
An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems |
2021 |
VLDB |
0.00010390548 |
| 1,902 |
Black or White? How to Develop an AutoTuner for Memory-based Analytics |
2020 |
SIGMOD |
0.00010157713 |
| 2,084 |
The Case for Predictive Database Systems: Opportunities and Challenges |
2011 |
CIDR |
9.5820534e-05 |
| 2,163 |
Elastic Machine Learning Algorithms in Amazon SageMaker |
2020 |
SIGMOD |
9.3949234e-05 |
| 3,269 |
iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases |
2019 |
VLDB |
7.2998062e-05 |
| 3,522 |
ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases |
2021 |
SIGMOD |
7.0096727e-05 |
| 4,549 |
Database-Agnostic Workload Management |
2019 |
CIDR |
6.0926728e-05 |
| 4,590 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.0620053e-05 |
| 5,473 |
Facilitating SQL Query Composition and Analysis |
2020 |
SIGMOD |
5.4885366e-05 |
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