# Towards Dynamic and Safe Configuration Tuning for Cloud Databases

arXiv: https://arxiv.org/abs/2203.14473 · HTML: https://mlsanity.com/paper/2203.14473/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2022.

## Built on by (most cited first, 16 of 16)

- [Transfer Learning for Bayesian Optimization: A Survey](https://mlsanity.com/paper/2302.05927/) (2023)
- [OpenBox: A Python Toolkit for Generalized Black-box Optimization](https://mlsanity.com/paper/2304.13339/) (2023)
- [E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model](https://mlsanity.com/paper/2404.11581/) (2024)
- [NeurDB: An AI-powered Autonomous Data System](https://mlsanity.com/paper/2405.03924/) (2024)
- [Is Large Language Model Good at Database Knob Tuning? A Comprehensive Experimental Evaluation](https://mlsanity.com/paper/2408.02213/) (2024)
- [A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning](https://mlsanity.com/paper/2303.05710/) (2023)
- [Dividable Configuration Performance Learning](https://mlsanity.com/paper/2409.07629/) (2024)
- [Navigating in High-Dimensional Search Space: A Hierarchical Bayesian Optimization Approach](https://mlsanity.com/paper/2410.23148/) (2024)
- [AI-Driven Research for Databases](https://mlsanity.com/paper/2604.06566/) (2026)
- [Towards General and Efficient Online Tuning for Spark](https://mlsanity.com/paper/2309.01901/) (2023)
- [L2T-Tune:LLM-Guided Hybrid Database Tuning with LHS and TD3](https://mlsanity.com/paper/2511.01602/) (2025)
- [Efficient End-to-End AutoML via Scalable Search Space Decomposition](https://mlsanity.com/paper/2206.09423/) (2022)
- [Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptions](https://mlsanity.com/paper/2510.22734/) (2025)
- [DemoTuner: Automatic Performance Tuning for Database Management Systems Based on Demonstration Reinforcement Learning](https://mlsanity.com/paper/2511.09998/) (2025)
- [Towards Anticipatory Databases Through Shared Data and Workload Semantics](https://mlsanity.com/paper/2609.14255/) (2026)
- [Accelerating Transfer-Learning-Based Autotuning with Predictive LLVM IR Performance Ranking](https://mlsanity.com/paper/2609.15807/) (2026)

## Abstract (arXiv)

Configuration knobs of database systems are essential to achieve high throughput and low latency. Recently, automatic tuning systems using machine learning methods (ML) have shown to find better configurations compared to experienced database administrators (DBAs). However, there are still gaps to apply the existing systems in production environments, especially in the cloud. First, they conduct tuning for a given workload within a limited time window and ignore the dynamicity of workloads and data. Second, they rely on a copied instance and do not consider the availability of the database when sampling configurations, making the tuning expensive, delayed, and unsafe. To fill these gaps, we propose OnlineTune, which tunes the online databases safely in changing cloud environments. To accommodate the dynamicity, OnlineTune embeds the environmental factors as context feature and adopts contextual Bayesian Optimization with context space partition to optimize the database adaptively and scalably. To pursue safety during tuning, we leverage the black-box and the white-box knowledge to evaluate the safety of configurations and propose a safe exploration strategy via subspace adaptation.%, greatly decreasing the risks of applying bad configurations. We conduct evaluations on dynamic workloads from benchmarks and real-world workloads. Compared with the state-of-the-art methods, OnlineTune achieves 14.4%~165.3% improvement on cumulative performance while reducing 91.0%~99.5% unsafe configuration recommendations.