# A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning

arXiv: https://arxiv.org/abs/2303.05710 · HTML: https://mlsanity.com/paper/2303.05710/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 75 references that are in the map.
- First posted on arXiv 10 Mar 2023.

## Builds on (most cited first, 21 of 21)

- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces](https://mlsanity.com/paper/1709.10163/) (2017)
- [An ADMM Based Framework for AutoML Pipeline Configuration](https://mlsanity.com/paper/1905.00424/) (2019)
- [OpenBox: A Generalized Black-box Optimization Service](https://mlsanity.com/paper/2106.00421/) (2021)
- [Learning to Optimize Join Queries With Deep Reinforcement Learning](https://mlsanity.com/paper/1808.03196/) (2018)
- [Efficient Automatic CASH via Rising Bandits](https://mlsanity.com/paper/2012.04371/) (2020)
- [Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources](https://mlsanity.com/paper/1802.10233/) (2018)
- [Deep Reinforcement Learning for Join Order Enumeration](https://mlsanity.com/paper/1803.00055/) (2018)
- [VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition](https://mlsanity.com/paper/2107.08861/) (2021)
- [Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation](https://mlsanity.com/paper/2110.12654/) (2021)
- [Towards Dynamic and Safe Configuration Tuning for Cloud Databases](https://mlsanity.com/paper/2203.14473/) (2022)
- [A Survey on Advancing the DBMS Query Optimizer: Cardinality Estimation, Cost Model, and Plan Enumeration](https://mlsanity.com/paper/2101.01507/) (2021)
- [LlamaTune: Sample-Efficient DBMS Configuration Tuning](https://mlsanity.com/paper/2203.05128/) (2022)
- [Transfer Learning based Search Space Design for Hyperparameter Tuning](https://mlsanity.com/paper/2206.02511/) (2022)
- [UDO: Universal Database Optimization using Reinforcement Learning](https://mlsanity.com/paper/2104.01744/) (2021)
- [Black or White? How to Develop an AutoTuner for Memory-based Analytics [Extended Version]](https://mlsanity.com/paper/2002.11780/) (2020)
- [Join Query Optimization with Deep Reinforcement Learning Algorithms](https://mlsanity.com/paper/1911.11689/) (2019)
- [Opportunistic View Materialization with Deep Reinforcement Learning](https://mlsanity.com/paper/1903.01363/) (2019)
- [A Review of Machine Learning-based Failure Management in Optical Networks](https://mlsanity.com/paper/2208.10677/) (2022)
- [Automated Database Indexing using Model-free Reinforcement Learning](https://mlsanity.com/paper/2007.14244/) (2020)
- [DBA bandits: Self-driving index tuning under ad-hoc, analytical workloads with safety guarantees](https://mlsanity.com/paper/2010.09208/) (2020)

## Built on by (most cited first, 4 of 4)

- [VDTuner: Automated Performance Tuning for Vector Data Management Systems](https://mlsanity.com/paper/2404.10413/) (2024)
- [Functional multi-armed bandit and the best function identification problems](https://mlsanity.com/paper/2503.00509/) (2025)
- [Towards General and Efficient Online Tuning for Spark](https://mlsanity.com/paper/2309.01901/) (2023)
- [DemoTuner: Automatic Performance Tuning for Database Management Systems Based on Demonstration Reinforcement Learning](https://mlsanity.com/paper/2511.09998/) (2025)

## Abstract (arXiv)

Recently using machine learning (ML) based techniques to optimize modern database management systems has attracted intensive interest from both industry and academia. With an objective to tune a specific component of a DBMS (e.g., index selection, knobs tuning), the ML-based tuning agents have shown to be able to find better configurations than experienced database administrators. However, one critical yet challenging question remains unexplored -- how to make those ML-based tuning agents work collaboratively. Existing methods do not consider the dependencies among the multiple agents, and the model used by each agent only studies the effect of changing the configurations in a single component. To tune different components for DBMS, a coordinating mechanism is needed to make the multiple agents cognizant of each other. Also, we need to decide how to allocate the limited tuning budget among the agents to maximize the performance. Such a decision is difficult to make since the distribution of the reward for each agent is unknown and non-stationary. In this paper, we study the above question and present a unified coordinating framework to efficiently utilize existing ML-based agents. First, we propose a message propagation protocol that specifies the collaboration behaviors for agents and encapsulates the global tuning messages in each agent's model. Second, we combine Thompson Sampling, a well-studied reinforcement learning algorithm with a memory buffer so that our framework can allocate budget judiciously in a non-stationary environment. Our framework defines the interfaces adapted to a broad class of ML-based tuning agents, yet simple enough for integration with existing implementations and future extensions. We show that it can effectively utilize different ML-based agents and find better configurations with 1.4~14.1X speedups on the workload execution time compared with baselines.