Autonomous Database Systems – A Systematic Review of Self-Healing and Self-Tuning Database Systems

Abstract

Problem Statement: Autonomous database systems represent a significant change in the management of databases, utilizing Machine Learning (ML) and Artificial Intelligence (AI) in order to carry out self-healing and self-tuning with minimal human intervention. Objectives: This systematic review investigates the defining characteristics, AI/ML techniques, challenges and the future trends of self-healing and self-tuning autonomous databases. Methodology: The research questions were answered integrating findings from 35 current literatures between 2020 and 2025. These literatures were obtained from several reputable databases. Results: From the study, self-healing databases employ techniques such as autoencoders, hidden Markov models, clustering algorithms, reinforcement learning, Bayesian optimization, neural networks and surrogate models to detect and recover from faults, enhancing operational resilience. On the other hand, self-tuning databases employ reinforcement learning, neural networks, multi-armed bandit techniques, decision trees, regression models, Bayesian optimization, and anomaly detection to optimize query execution, indexing, and resource allocation. Challenges in applying AI/ML in autonomous databases study include data quality dependencies, and adaptation to dynamic workload still exists and integration into existing infrastructures. Conclusion: The deeper integration of deep learning techniques and predictive modelling serves as future trends to improve this autonomy.