Let AI Tune Your Database Management System for You

Reinforcement Learning (RL) is transforming DBMS configuration tuning by enabling adaptive, real-time decision-making. Solutions like CDBTune, Qtune, and HUNTER use RL models such as DDPG to optimize performance in large, complex configuration spaces with limited historical data. By interacting with the environment and receiving rewards based on system performance, RL-based systems offer a dynamic approach to tuning, improving throughput and latency over time.

Table of Links

Abstract and 1 Introduction

1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions

2 Tuning Objectives

3 Overview of Tuning Framework

4 Workload Characterization and 4.1 Query-level Characterization

4.2 Runtime-based Characterization

5 Feature Pruning and 5.1 Workload-level Pruning

5.2 Configuration-level Pruning

5.3 Summary

6 Knowledge from Experience

7 Configuration Recommendation and 7.1 Bayesian Optimization

7.2 Neural Network

7.3 Reinforcement Learning

7.4 Search-based Solutions ...