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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
h33d2d7e17c795e5e
Venue
SIGMOD
Year
2021
Pagerank
6.8804723e-05
Overall Rank
3,959 | 73.47%
DOI
10.1145/3448016.3457276
PDF
Download (CC BY 4.0)

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
2,995 Panda: Performance Debugging for Databases using LLM Agents 2024 CIDR 7.7556154e-05
3,486 HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements 2022 SIGMOD 7.2428834e-05
3,575 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 7.1769215e-05
4,101 Towards Dynamic and Safe Configuration Tuning for Cloud Databases 2022 SIGMOD 6.7930727e-05
4,508 LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning 2022 SIGMOD 6.5553854e-05
5,043 Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing 2022 VLDB 6.28918e-05
5,098 DBPA: A Benchmark for Transactional Database Performance Anomalies 2023 SIGMOD 6.2631174e-05
5,713 PilotScope: Steering Databases with Machine Learning Drivers 2024 VLDB 6.0090633e-05
7,169 Cost-Intelligent Data Analytics in the Cloud 2024 CIDR 5.5789764e-05
7,621 SageDB: An Instance-Optimized Data Analytics System 2022 VLDB 5.4664683e-05
7,715 Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems 2022 SIGMOD 5.4556951e-05
7,925 CAMAL: Optimizing LSM-trees via Active Learning 2024 SIGMOD 5.4086685e-05
7,997 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.3938336e-05
8,571 Serverless State Management Systems 2024 CIDR 5.2989007e-05
8,589 Robust and Budget-Constrained Encoding Configurations for In-Memory Database Systems 2022 VLDB 5.2937865e-05
8,638 Limousine: Blending Learned and Classical Indexes to Self-Design Larger-than-Memory Cloud Storage Engines 2024 SIGMOD 5.2825944e-05
8,902 Tiresias: Enabling Predictive Autonomous Storage and Indexing 2022 VLDB 5.2387388e-05
9,154 Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems 2024 VLDB 5.2050937e-05
9,731 Database Gyms 2023 CIDR 5.120066e-05
10,323 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 5.0241309e-05
10,366 SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression 2025 VLDB 5.0091669e-05
11,207 Automated Database Tuning vs. Human-Based Tuning in a Simulated Stressful Work Environment: A Demonstration of the Database Gym 2025 SIGMOD 4.9605343e-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
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061463175
21 Efficiently Compiling Efficient Query Plans for Modern Hardware 2011 VLDB 0.00056737487
74 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00037164008
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035920657
142 Neo: A Learned Query Optimizer 2019 VLDB 0.00029114937
200 Snorkel: Rapid Training Data Creation with Weak Supervision 2018 VLDB 0.00025443474
224 Self-Driving Database Management Systems 2017 CIDR 0.00023973185
236 OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases 2014 VLDB 0.00023639649
309 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.0002143249
313 Tuning Database Configuration Parameters with iTuned 2009 VLDB 0.00021288111
320 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021128745
405 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019020138
411 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.0001874043
453 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001801482
458 Query-based Workload Forecasting for Self-Driving Database Management Systems 2018 SIGMOD 0.00017870238
545 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.0001657599
628 Performance Prediction for Concurrent Database Workloads 2011 SIGMOD 0.00015421836
676 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.00014838681
690 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014703258
757 DBSherlock: A Performance Diagnostic Tool for Transactional Databases 2016 SIGMOD 0.00014202187
775 Self-tuning Database Technology and Information Services: from Wishful Thinking to Viable Engineering 2002 VLDB 0.00014062388
863 An Empirical Evaluation of In-Memory Multi-Version Concurrency Control 2017 VLDB 0.00013368866
1,436 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010658836
1,517 Automatically Indexing Millions of Databases in Microsoft Azure SQL Database 2019 SIGMOD 0.00010385462
1,700 Black or White? How to Develop an AutoTuner for Memory-based Analytics 2020 SIGMOD 9.8224761e-05
1,940 Performance and Resource Modeling in Highly-Concurrent OLTP Workloads 2013 SIGMOD 9.3179491e-05
2,290 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 8.6813363e-05
2,768 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 8.0199086e-05
3,384 Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads 2013 VLDB 7.3392425e-05
4,817 Permutable Compiled Queries: Dynamically Adapting Compiled Queries without Recompiling 2021 VLDB 6.388743e-05
5,670 Uncertainty Aware Query Execution Time Prediction 2014 VLDB 6.0266818e-05
6,560 Workflow Management with Service Quality Guarantees 2002 SIGMOD 5.7288882e-05
8,812 Automatic Workload Driven Index Defragmentation 2011 VLDB 5.25724e-05
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