International Journal on Science and Technology

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Self-Healing Databases Automating DB Maintenance with AI

Author(s) Sai Kalyani Rachapalli
Country United States
Abstract The increasing complexity of database systems, coupled with the growing demand for high availability and minimal downtime, has made the automation of database maintenance crucial. Traditional database management methods are predominantly manual, involving significant human intervention to resolve performance bottlenecks, data inconsistencies, and failure recovery. However, self-healing databases, which incorporate artificial intelligence (AI) and machine learning (ML), provide a promising solution by enabling autonomous detection and correction of issues. This paper explores the emerging concept of self-healing databases, focusing on how AI-driven technologies can automate various maintenance tasks, such as error detection, performance optimization, and failure recovery. The paper investigates current methodologies, existing challenges, and the potential future applications of self-healing databases in production environments. By automating key database processes, self-healing systems offer the potential to reduce the operational costs of database maintenance, improve system resilience, and minimize downtime. Furthermore, the paper discusses the scalability of AI in database management, its ability to predict issues before they occur, and the integration of reinforcement learning for dynamic system optimization.
Field Engineering
Published In Volume 15, Issue 1, January-March 2024
Published On 2024-01-05
DOI https://doi.org/10.71097/IJSAT.v15.i1.4679
Short DOI https://doi.org/g9hjds
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