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MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems

Summary: MB2's ModelBot2 presents a decomposed, end-to-end ML framework for self-driving DBMSs, using fine-grained units to predict behavior for unseen configurations. It provides offline data generation and in-memory deployment, delivering up to 25x accuracy against state-of-the-art models for OLTP/OLAP in dynamic workloads. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6229
Venue
SIGMOD
Year
2021
Pagerank
6.987575e-05
Overall Rank
3,961 | 72.83%
DOI
10.1145/3448016.3457276

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{ma_sigmod21,
        title = {{MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems}},
        author = {Ma, Lin and Zhang, William and Jiao, Jie and Wang, Wuwen and Butrovich, Matthew and Lim, Wan Shen and Menon, Prashanth and Pavlo, Andrew},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457276},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457276},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 22 of 22 citing papers.

Rank Citing Paper Year Venue Pagerank
3,586 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 7.2834069e-05
3,587 HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements 2022 SIGMOD 7.2829345e-05
3,757 Panda: Performance Debugging for Databases using LLM Agents 2024 CIDR 7.1483644e-05
4,011 Towards Dynamic and Safe Configuration Tuning for Cloud Databases 2022 SIGMOD 6.959982e-05
5,023 DBPA: A Benchmark for Transactional Database Performance Anomalies 2023 SIGMOD 6.3979497e-05
5,169 LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning 2022 SIGMOD 6.3349035e-05
5,388 Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing 2022 VLDB 6.2362811e-05
6,462 PilotScope: Steering Databases with Machine Learning Drivers 2024 VLDB 5.8717744e-05
7,589 Cost-Intelligent Data Analytics in the Cloud 2024 CIDR 5.5907048e-05
7,757 CAMAL: Optimizing LSM-trees via Active Learning 2024 SIGMOD 5.5508469e-05
7,785 Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems 2022 SIGMOD 5.5450355e-05
7,846 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.5331459e-05
8,323 SageDB: An Instance-Optimized Data Analytics System 2022 VLDB 5.4539294e-05
8,375 Serverless State Management Systems 2024 CIDR 5.4391978e-05
8,410 Robust and Budget-Constrained Encoding Configurations for In-Memory Database Systems 2022 VLDB 5.4309397e-05
8,463 Limousine: Blending Learned and Classical Indexes to Self-Design Larger-than-Memory Cloud Storage Engines 2024 SIGMOD 5.4205593e-05
8,783 Tiresias: Enabling Predictive Autonomous Storage and Indexing 2022 VLDB 5.3740362e-05
8,984 Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems 2024 VLDB 5.3395569e-05
9,536 Database Gyms 2023 CIDR 5.2529727e-05
10,105 SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression 2025 VLDB 5.1435736e-05
10,506 This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch! 2026 SIGMOD 5.093636e-05
10,706 Automated Database Tuning vs. Human-Based Tuning in a Simulated Stressful Work Environment: A Demonstration of the Database Gym 2025 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 33 of 33 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
18 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059284255
23 Efficiently Compiling Efficient Query Plans for Modern Hardware 2011 VLDB 0.00054886415
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035838391
86 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00035316107
154 Neo: A Learned Query Optimizer 2019 VLDB 0.00028726181
205 Snorkel: Rapid Training Data Creation with Weak Supervision 2018 VLDB 0.00025235185
226 OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases 2014 VLDB 0.00024027277
234 Self-Driving Database Management Systems 2017 CIDR 0.00023810722
323 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021264788
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
401 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019092557
461 Query-based Workload Forecasting for Self-Driving Database Management Systems 2018 SIGMOD 0.00018068441
465 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001803934
498 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.00017440583
563 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.0001650812
624 Performance Prediction for Concurrent Database Workloads 2011 SIGMOD 0.00015683402
682 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.00015014887
697 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014888851
781 Self-tuning Database Technology and Information Services: from Wishful Thinking to Viable Engineering 2002 VLDB 0.00014085674
798 DBSherlock: A Performance Diagnostic Tool for Transactional Databases 2016 SIGMOD 0.00013919795
903 An Empirical Evaluation of In-Memory Multi-Version Concurrency Control 2017 VLDB 0.0001332486
1,468 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010686496
1,548 Automatically Indexing Millions of Databases in Microsoft Azure SQL Database 2019 SIGMOD 0.00010392475
1,686 Black or White? How to Develop an AutoTuner for Memory-based Analytics 2020 SIGMOD 0.00010008686
1,931 Performance and Resource Modeling in Highly-Concurrent OLTP Workloads 2013 SIGMOD 9.4664741e-05
2,313 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 8.762627e-05
2,812 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 8.0979597e-05
3,482 Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads 2013 VLDB 7.3751635e-05
5,000 Permutable Compiled Queries: Dynamically Adapting Compiled Queries without Recompiling 2021 VLDB 6.4069917e-05
5,537 Uncertainty Aware Query Execution Time Prediction 2014 VLDB 6.1820087e-05
6,424 Workflow Management with Service Quality Guarantees 2002 SIGMOD 5.8811027e-05
8,641 Automatic Workload Driven Index Defragmentation 2011 VLDB 5.3942658e-05
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