In the modern digital era, cloud databases serve as the foundational layer for business-critical applications, demanding uninterrupted availability, dynamic scalability, and fault tolerance. The concept and architecture of self-healing cloud databases emphasize their role in minimizing downtime and ensuring business continuity. Traditional reactive fault recovery approaches are increasingly insufficient in handling today’s complex, distributed, and real-time cloud systems. Self-healing databases leverage automation, real-time monitoring, and artificial intelligence (AI) to proactively detect, diagnose, and autonomously recover from faults. The study details enabling technologies such as predictive analytics, observability frameworks, container orchestration (e.g., Kubernetes), and DevOps-SRE integrations that make automated remediation possible. Additionally, techniques like load balancing, elastic resource provisioning, and auto-scaling are examined to demonstrate resilience in dynamic workloads. such as anomaly classification complexity and cross-architecture generalization. Evaluation metrics including MTTR, MTTD, fault recovery rate, and uptime percentage are presented to quantify effectiveness. The paper concludes that the convergence of AI, cloud-native infrastructure, and proactive automation is essential for the next generation of resilient database systems capable of responding to operational anomalies autonomously, thus supporting continuous service availability for non-stop business operations.
Veeravenkata Maruthi Lakshmi Ganesh Nerella (Sat,) studied this question.