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HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements
Summary: HUNTER is an online, hybrid tuner for cloud DBs addressing personalized constraints and workload heterogeneity. It uses GA-warmed samples to bootstrap deep RL, with PCA, Random Forest, and a Fast Exploration Strategy to prune the search space, plus clone-based parallel testing for fast, safe online tuning; up to 2.8x (1 clone) and 22.8x (20 clones) speedups.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6334
- Venue
- SIGMOD
- Year
- 2022
- Pagerank
- 6.2225151e-05
- Overall Rank
- 4,399 | 69.40%
- DOI
-
10.1145/3514221.3517882
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 18 of 18 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 |
| 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 |
| 7,753 |
Rethinking Learned Cost Models: Why Start from Scratch? |
2023 |
SIGMOD |
4.660151e-05 |
| 8,103 |
Grep: A Graph Learning Based Database Partitioning System |
2023 |
SIGMOD |
4.5852201e-05 |
| 9,352 |
Db2une: Tuning Under Pressure via Deep Learning |
2024 |
VLDB |
4.3522361e-05 |
| 9,956 |
SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression |
2025 |
VLDB |
4.2373024e-05 |
| 10,031 |
PGTuner: An Efficient Framework for Automatic and Transferable Configuration Tuning of Proximity Graphs |
2026 |
SIGMOD |
4.1945683e-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 |
| 10,259 |
Scarf: Self-Adaptive Tuning via Multi-Objective Reinforcement Learning for Apache Flink |
2026 |
VLDB |
4.1945683e-05 |
| 10,301 |
DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning |
2026 |
VLDB |
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 |
| 10,668 |
Twisted Twin: A Collaborative and Competitive Memory Management Approach in HTAP Systems |
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 |
| 102 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049545203 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036721403 |
| 237 |
An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server |
1997 |
VLDB |
0.00031726304 |
| 333 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027206884 |
| 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 |
| 663 |
Adaptive Self-Tuning Memory in DB2 |
2006 |
VLDB |
0.00018469455 |
| 716 |
Query-based Workload Forecasting for Self-Driving Database Management Systems |
2018 |
SIGMOD |
0.00017723171 |
| 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 |
| 826 |
ALEX: An Updatable Adaptive Learned Index |
2020 |
SIGMOD |
0.00016224841 |
| 1,022 |
DBSherlock: A Performance Diagnostic Tool for Transactional Databases |
2016 |
SIGMOD |
0.00014614917 |
| 1,254 |
Selectivity Estimation for Range Predicates using Lightweight Models |
2019 |
VLDB |
0.00013027411 |
| 1,311 |
Dostoevsky: Better Space-Time Trade-Offs for LSM-Tree Based Key-Value Stores via Adaptive Removal of Superfluous Merging |
2018 |
SIGMOD |
0.00012657439 |
| 1,375 |
FITing-Tree: A Data-aware Index Structure |
2019 |
SIGMOD |
0.00012303141 |
| 1,827 |
An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems |
2021 |
VLDB |
0.00010390548 |
| 2,047 |
Automatically Indexing Millions of Databases in Microsoft Azure SQL Database |
2019 |
SIGMOD |
9.6920209e-05 |
| 2,083 |
Towards a Learning Optimizer for Shared Clouds |
2019 |
VLDB |
9.5834572e-05 |
| 2,109 |
The Log-Structured Merge-Bush & the Wacky Continuum |
2019 |
SIGMOD |
9.5318694e-05 |
| 2,865 |
Designing Succinct Secondary Indexing Mechanism by Exploiting Column Correlations |
2019 |
SIGMOD |
7.9862595e-05 |
| 3,142 |
Active Learning for ML Enhanced Database Systems |
2020 |
SIGMOD |
7.4815444e-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 |
| 4,415 |
Semi-Automatic Index Tuning: Keeping DBAs in the Loop |
2012 |
VLDB |
6.205081e-05 |
| 4,590 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.0620053e-05 |
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