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Black or White? How to Develop an AutoTuner for Memory-based Analytics
Summary: RelM, a white-box memory autotuner, exploits interactions from containers to JVM for near-optimal tuning with low overhead. Guided-BO speeds Bayesian optimization with RelM; Spark tests show near-brute-force quality at reduced cost.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 5805
- Venue
- SIGMOD
- Year
- 2020
- Pagerank
- 0.00010176219
- Overall Rank
- 1,892 | 86.86%
- DOI
-
10.1145/3318464.3380591
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 26 of 26 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 1,816 |
An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems |
2021 |
VLDB |
0.00010438512 |
| 3,105 |
GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization |
2024 |
VLDB |
7.5567226e-05 |
| 3,466 |
AI Meets Database: AI4DB and DB4AI |
2021 |
SIGMOD |
7.0645718e-05 |
| 3,655 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
6.8723042e-05 |
| 3,995 |
ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases |
2021 |
SIGMOD |
6.5475871e-05 |
| 4,431 |
Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process |
2022 |
SIGMOD |
6.1870601e-05 |
| 4,587 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.0594195e-05 |
| 4,799 |
Towards Dynamic and Safe Configuration Tuning for Cloud Databases |
2022 |
SIGMOD |
5.9082876e-05 |
| 4,870 |
DBPA: A Benchmark for Transactional Database Performance Anomalies |
2023 |
SIGMOD |
5.8583776e-05 |
| 5,318 |
LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL Applications |
2022 |
SIGMOD |
5.5685434e-05 |
| 5,787 |
Machine Learning for Databases |
2021 |
VLDB |
5.3256401e-05 |
| 5,834 |
An Efficient Transfer Learning Based Configuration Adviser for Database Tuning |
2024 |
VLDB |
5.3082111e-05 |
| 5,941 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
5.2594013e-05 |
| 6,298 |
Towards instance-optimized data systems |
2021 |
VLDB |
5.1182917e-05 |
| 6,376 |
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning |
2023 |
SIGMOD |
5.0861082e-05 |
| 6,489 |
Towards General and Efficient Online Tuning for Spark |
2023 |
VLDB |
5.0373773e-05 |
| 7,676 |
E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model |
2025 |
VLDB |
4.6770108e-05 |
| 8,095 |
Grep: A Graph Learning Based Database Partitioning System |
2023 |
SIGMOD |
4.5837691e-05 |
| 8,585 |
A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning |
2024 |
VLDB |
4.4856045e-05 |
| 9,732 |
ContTune: Continuous Tuning by Conservative Bayesian Optimization for Distributed Stream Data Processing Systems |
2023 |
VLDB |
4.2901665e-05 |
| 10,093 |
MCTuner: Spatial Decomposition-Enhanced Database Tuning via LLM-Guided Exploration |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,382 |
Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptions |
2025 |
SIGMOD |
4.1905499e-05 |
| 10,641 |
AQETuner: Reliable Query-level Configuration Tuning for Analytical Query Engines |
2025 |
VLDB |
4.1905499e-05 |
| 11,059 |
Agile-Ant: Self-managing Distributed Cache Management for Cost Optimization of Big Data Applications |
2024 |
VLDB |
4.1905499e-05 |
| 11,343 |
Juggler: Autonomous Cost Optimization and Performance Prediction of Big Data Applications |
2022 |
SIGMOD |
4.1905499e-05 |
| 11,408 |
SparkCAD: Caching Anomalies Detector for Spark Applications |
2022 |
VLDB |
4.1905499e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 16 of 16 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| 3,995 |
ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases |
2021 |
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6.5475871e-05 |
| 4,587 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
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M3: Scaling Up Machine Learning via Memory Mapping |
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