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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
5866
Venue
SIGMOD
Year
2020
Pagerank
0.00010008686
Overall Rank
1,686 | 88.44%
DOI
10.1145/3318464.3380591

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{kunjir_sigmod20,
        title = {{Black or White? How to Develop an AutoTuner for Memory-based Analytics}},
        author = {Kunjir, Mayuresh and Babu, Shivnath},
        series = {{SIGMOD} '20},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3318464.3380591},
        url = {https://dl.acm.org/doi/10.1145/3318464.3380591},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 27 of 27 citing papers.

Rank Citing Paper Year Venue Pagerank
1,344 An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems 2021 VLDB 0.00011094717
2,298 GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization 2024 VLDB 8.7886538e-05
2,740 Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation 2022 VLDB 8.1855759e-05
2,888 AI Meets Database: AI4DB and DB4AI 2021 SIGMOD 7.9941489e-05
3,116 ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases 2021 SIGMOD 7.7390737e-05
3,961 MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems 2021 SIGMOD 6.987575e-05
4,011 Towards Dynamic and Safe Configuration Tuning for Cloud Databases 2022 SIGMOD 6.959982e-05
4,368 Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process 2022 SIGMOD 6.7393882e-05
4,854 LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL Applications 2022 SIGMOD 6.4779623e-05
5,023 DBPA: A Benchmark for Transactional Database Performance Anomalies 2023 SIGMOD 6.3979497e-05
5,171 An Efficient Transfer Learning Based Configuration Adviser for Database Tuning 2024 VLDB 6.3347618e-05
5,340 Machine Learning for Databases 2021 VLDB 6.2603359e-05
5,573 Eraser: Eliminating Performance Regression on Learned Query Optimizer 2024 VLDB 6.1682747e-05
5,974 Towards instance-optimized data systems 2021 VLDB 6.0230488e-05
6,344 Towards General and Efficient Online Tuning for Spark 2023 VLDB 5.9060457e-05
6,600 A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning 2023 SIGMOD 5.8250114e-05
6,997 E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model 2025 VLDB 5.7300324e-05
8,042 Grep: A Graph Learning Based Database Partitioning System 2023 SIGMOD 5.5015896e-05
8,615 A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning 2024 VLDB 5.4005602e-05
9,898 ContTune: Continuous Tuning by Conservative Bayesian Optimization for Distributed Stream Data Processing Systems 2023 VLDB 5.1997534e-05
10,253 HAMMER: An Automatic RAG Tuning System via Hierarchical Memory-Guided Monte Carlo Tree Search 2026 SIGMOD 5.093636e-05
10,384 MCTuner: Spatial Decomposition-Enhanced Database Tuning via LLM-Guided Exploration 2026 SIGMOD 5.093636e-05
10,660 Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptions 2025 SIGMOD 5.093636e-05
10,886 AQETuner: Reliable Query-level Configuration Tuning for Analytical Query Engines 2025 VLDB 5.093636e-05
11,264 Agile-Ant: Self-managing Distributed Cache Management for Cost Optimization of Big Data Applications 2024 VLDB 5.093636e-05
11,539 Juggler: Autonomous Cost Optimization and Performance Prediction of Big Data Applications 2022 SIGMOD 5.093636e-05
11,603 SparkCAD: Caching Anomalies Detector for Spark Applications 2022 VLDB 5.093636e-05
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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.

Rank Cited Paper Year Venue Pagerank
86 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00035316107
87 Automated Selection of Materialized Views and Indexes for SQL Databases 2000 VLDB 0.00035281619
154 Neo: A Learned Query Optimizer 2019 VLDB 0.00028726181
156 An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server 1997 VLDB 0.00028636811
246 Automating Physical Database Design in a Parallel Database 2002 SIGMOD 0.00023457421
334 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00020875082
347 Tuning Database Configuration Parameters with iTuned 2009 VLDB 0.00020651582
498 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.00017440583
523 Adaptive Self-Tuning Memory in DB2 2006 VLDB 0.00017133451
625 Automatic Performance Diagnosis and Tuning in Oracle 2005 CIDR 0.0001566968
781 Self-tuning Database Technology and Information Services: from Wishful Thinking to Viable Engineering 2002 VLDB 0.00014085674
923 Starfish: A Self-tuning System for Big Data Analytics 2011 CIDR 0.00013189886
7,546 MRTuner: A Toolkit to Enable Holistic Optimization for MapReduce Jobs 2014 VLDB 5.6024593e-05
7,673 ROBUS: Fair Cache Allocation for Data-parallel Workloads 2017 SIGMOD 5.5705944e-05
9,039 Tempo: Robust and Self-Tuning Resource Management in Multi-tenant Parallel Databases 2016 VLDB 5.3267474e-05
9,982 Thoth in Action: Memory Management in Modern Data Analytics 2017 VLDB 5.1844786e-05
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