Back to papers
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
- 5804
- Venue
- SIGMOD
- Year
- 2020
- Pagerank
- 0.00010157713
- Overall Rank
- 1,902 | 86.77%
- 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 |
| 1,827 |
An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems |
2021 |
VLDB |
0.00010390548 |
| 3,114 |
GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization |
2024 |
VLDB |
7.5451724e-05 |
| 3,473 |
AI Meets Database: AI4DB and DB4AI |
2021 |
SIGMOD |
7.062864e-05 |
| 3,522 |
ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases |
2021 |
SIGMOD |
7.0096727e-05 |
| 3,812 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
6.7373184e-05 |
| 4,434 |
Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process |
2022 |
SIGMOD |
6.1929999e-05 |
| 4,590 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.0620053e-05 |
| 4,842 |
Towards Dynamic and Safe Configuration Tuning for Cloud Databases |
2022 |
SIGMOD |
5.8826802e-05 |
| 4,868 |
DBPA: A Benchmark for Transactional Database Performance Anomalies |
2023 |
SIGMOD |
5.8629636e-05 |
| 5,833 |
LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL Applications |
2022 |
SIGMOD |
5.3106182e-05 |
| 5,861 |
Machine Learning for Databases |
2021 |
VLDB |
5.298883e-05 |
| 5,952 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
5.2591691e-05 |
| 6,151 |
An Efficient Transfer Learning Based Configuration Adviser for Database Tuning |
2024 |
VLDB |
5.183652e-05 |
| 6,297 |
Towards instance-optimized data systems |
2021 |
VLDB |
5.1227886e-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 |
| 8,103 |
Grep: A Graph Learning Based Database Partitioning System |
2023 |
SIGMOD |
4.5852201e-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 |
| 9,733 |
ContTune: Continuous Tuning by Conservative Bayesian Optimization for Distributed Stream Data Processing Systems |
2023 |
VLDB |
4.2942813e-05 |
| 10,093 |
MCTuner: Spatial Decomposition-Enhanced Database Tuning via LLM-Guided Exploration |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,370 |
Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptions |
2025 |
SIGMOD |
4.1945683e-05 |
| 10,633 |
AQETuner: Reliable Query-level Configuration Tuning for Analytical Query Engines |
2025 |
VLDB |
4.1945683e-05 |
| 11,056 |
Agile-Ant: Self-managing Distributed Cache Management for Cost Optimization of Big Data Applications |
2024 |
VLDB |
4.1945683e-05 |
| 11,341 |
Juggler: Autonomous Cost Optimization and Performance Prediction of Big Data Applications |
2022 |
SIGMOD |
4.1945683e-05 |
| 11,405 |
SparkCAD: Caching Anomalies Detector for Spark Applications |
2022 |
VLDB |
4.1945683e-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.
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 4,590 |
MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems |
2021 |
SIGMOD |
6.0620053e-05 |
| 3,522 |
ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases |
2021 |
SIGMOD |
7.0096727e-05 |
| 13,343 |
M3: Scaling Up Machine Learning via Memory Mapping |
2016 |
SIGMOD |
- |
| 1,827 |
An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems |
2021 |
VLDB |
0.00010390548 |
| 4,842 |
Towards Dynamic and Safe Configuration Tuning for Cloud Databases |
2022 |
SIGMOD |
5.8826802e-05 |
| 4,593 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
6.0606891e-05 |
| 6,456 |
From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems |
2019 |
SIGMOD |
5.0564619e-05 |
| 6,871 |
Towards General and Efficient Online Tuning for Spark |
2023 |
VLDB |
4.8997004e-05 |
| 4,802 |
Resource Elasticity for Large-Scale Machine Learning |
2015 |
SIGMOD |
5.9114415e-05 |
| 6,268 |
Speedup Your Analytics: Automatic Parameter Tuning for Databases and Big Data Systems |
2019 |
VLDB |
5.133857e-05 |