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

EasyChair Preprint 15856

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 incorpo-
rated to model interdependencies within database components,
ensuring holistic recovery strategies. Data efficiency was en-
hanced 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 improve-
ments in adaptability, efficiency, and reliability, contributing to
advancements in database management and self-healing systems.

Keyphrases: Cascading Failure Prediction, Database Dependency Modeling, Database Management Systems(DBMS), Dynamic Workload Adaptation, Explainable AI (XAI), Graph Neural Networks (GNNs), Model-Agnostic Meta-Learning (MAML), Proactive Anomaly Prevention, RL-Based Recovery, Real-Time Adaptability, Recovery Optimization, Reinforcement Learning (RL), Scalable Database Systems, Self-Healing Databases, Task Generalization, anomaly detection, federated meta-learning, meta-learning, multi-objective optimization

@booklet{EasyChair:15856,
  author    = {Joydeep Chandra and Prabal Manhas and Aastha Sharma and Rashi Sahay},
  title     = {Efficient and Scalable Self-Healing Databases Using Meta-Learning and Dependency-Driven Recovery},
  howpublished = {EasyChair Preprint 15856},
  year      = {EasyChair, 2025}}