Efficient and Scalable Self-Healing Databases Using Meta-Learning and Dependency-Driven Recovery

Abstract: This study explored the development of a novel self-healing framework for databases using meta-learning and reinforcement learning techniques. The primary objective was to address the challenges of real-time adaptability and minimal retraining in dynamic workload environments. The proposed approach integrated Model-Agnostic Meta-Learning (MAML) with reinforcement learning to enable anomaly detection and corrective actions that adapted swiftly to evolving database conditions. Multi-objective optimization was employed to balance performance, resource utilization, and cost efficiency during the healing process. Graph Neural Networks (GNNs) were incorporated to model interdependencies within database components, ensuring holistic recovery strategies. Data efficiency was enhanced through synthetic task augmentation and self-supervised learning, enabling effective training in sparse data regimes. To promote trust and transparency, explainable AI techniques were integrated to provide interpretable insights into anomaly detection and healing actions. Federated meta-learning further enabled privacy-preserving adaptability in distributed database environments. The framework demonstrated significant improvements in adaptability, efficiency, and reliability, contributing to advancements in database management and self-healing systems.