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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
6.8918628e-05
Overall Rank
3,965 | 73.35%
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
3,169 Panda: Performance Debugging for Databases using LLM Agents 2024 CIDR 7.5696238e-05
3,486 HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements 2022 SIGMOD 7.2636102e-05
3,590 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 7.1865343e-05
4,079 Towards Dynamic and Safe Configuration Tuning for Cloud Databases 2022 SIGMOD 6.818264e-05
4,482 LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning 2022 SIGMOD 6.5802486e-05
5,058 Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing 2022 VLDB 6.2926774e-05
5,077 DBPA: A Benchmark for Transactional Database Performance Anomalies 2023 SIGMOD 6.2858696e-05
5,871 PilotScope: Steering Databases with Machine Learning Drivers 2024 VLDB 5.9639223e-05
7,160 Cost-Intelligent Data Analytics in the Cloud 2024 CIDR 5.5962258e-05
7,598 SageDB: An Instance-Optimized Data Analytics System 2022 VLDB 5.4871733e-05
7,905 CAMAL: Optimizing LSM-trees via Active Learning 2024 SIGMOD 5.4291824e-05
7,915 Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems 2022 SIGMOD 5.4276987e-05
7,977 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.4142519e-05
8,542 Serverless State Management Systems 2024 CIDR 5.3189983e-05
8,563 Robust and Budget-Constrained Encoding Configurations for In-Memory Database Systems 2022 VLDB 5.3138647e-05
8,620 Limousine: Blending Learned and Classical Indexes to Self-Design Larger-than-Memory Cloud Storage Engines 2024 SIGMOD 5.301557e-05
8,872 Tiresias: Enabling Predictive Autonomous Storage and Indexing 2022 VLDB 5.2584643e-05
9,133 Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems 2024 VLDB 5.2229655e-05
9,706 Database Gyms 2023 CIDR 5.1376763e-05
10,290 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 5.0431863e-05
10,333 SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression 2025 VLDB 5.0281656e-05
11,139 Automated Database Tuning vs. Human-Based Tuning in a Simulated Stressful Work Environment: A Demonstration of the Database Gym 2025 SIGMOD 4.9793485e-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 0.00061066921
21 Efficiently Compiling Efficient Query Plans for Modern Hardware 2011 VLDB 0.00056855599
78 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00036684414
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035864347
145 Neo: A Learned Query Optimizer 2019 VLDB 0.0002908188
205 Snorkel: Rapid Training Data Creation with Weak Supervision 2018 VLDB 0.00025181304
224 Self-Driving Database Management Systems 2017 CIDR 0.00024013745
235 OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases 2014 VLDB 0.00023697028
314 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00021282642
318 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021167555
322 Tuning Database Configuration Parameters with iTuned 2009 VLDB 0.00021041865
406 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019045544
437 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.00018315867
460 Query-based Workload Forecasting for Self-Driving Database Management Systems 2018 SIGMOD 0.00017842695
461 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00017829982
560 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00016403151
629 Performance Prediction for Concurrent Database Workloads 2011 SIGMOD 0.00015429949
682 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.0001481781
692 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014741011
780 Self-tuning Database Technology and Information Services: from Wishful Thinking to Viable Engineering 2002 VLDB 0.00014025444
782 DBSherlock: A Performance Diagnostic Tool for Transactional Databases 2016 SIGMOD 0.00014022168
872 An Empirical Evaluation of In-Memory Multi-Version Concurrency Control 2017 VLDB 0.00013342029
1,433 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010677711
1,516 Automatically Indexing Millions of Databases in Microsoft Azure SQL Database 2019 SIGMOD 0.00010402594
1,699 Black or White? How to Develop an AutoTuner for Memory-based Analytics 2020 SIGMOD 9.8445322e-05
1,939 Performance and Resource Modeling in Highly-Concurrent OLTP Workloads 2013 SIGMOD 9.3331247e-05
2,275 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 8.7090584e-05
2,842 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 7.949193e-05
3,519 Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads 2013 VLDB 7.2389387e-05
4,793 Permutable Compiled Queries: Dynamically Adapting Compiled Queries without Recompiling 2021 VLDB 6.4127583e-05
5,656 Uncertainty Aware Query Execution Time Prediction 2014 VLDB 6.0488629e-05
6,549 Workflow Management with Service Quality Guarantees 2002 SIGMOD 5.7503218e-05
8,785 Automatic Workload Driven Index Defragmentation 2011 VLDB 5.2769169e-05
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