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

arXiv: https://arxiv.org/abs/2507.13757 · HTML: https://mlsanity.com/paper/2507.13757/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2025.

## Built on by (most cited first, 1 of 1)

- [Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation](https://mlsanity.com/paper/2507.20014/) (2025)

## Abstract (arXiv)

Modern database management systems (DBMS) face significant challenges in maintaining performance and availability under dynamic workloads. This paper proposes a novel self-healing framework that integrates Model-Agnostic Meta-Learning (MAML) for few-shot anomaly detection, Graph Neural Networks (GNNs) for dependency-driven cascading failure prediction, and multi-objective Reinforcement Learning (RL) for autonomous recovery. Unlike existing database tuning systems that focus primarily on offline configuration optimization, our framework enables real-time, end-to-end self-healing by rapidly adapting to unseen workload patterns with minimal labeled data. We introduce dynamic GNN-based dependency modeling that captures workload-dependent relationships between database components, enabling proactive cascade prevention. A scalarized multi-objective RL formulation balances latency, resource utilization, and cost during recovery, while SHAP-based explainability ensures operational transparency. Evaluations on Google Cluster Data and TPC benchmarks demonstrate 90.5% anomaly detection F1-score with 5-shot adaptation, 90.1% cascade prediction accuracy, and 85.1% latency reduction in recovery actions, outperforming strong baselines including Isolation Forest, LSTM autoencoders, static GCN, and standard RL methods.