{"ID":2893013,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.13757","arxiv_id":"2507.13757","title":"Efficient and Scalable Self-Healing Databases Using Meta-Learning and Dependency-Driven Recovery","abstract":"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.","short_abstract":"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-dr...","url_abs":"https://arxiv.org/abs/2507.13757","url_pdf":"https://arxiv.org/pdf/2507.13757v3","authors":"[\"Joydeep Chandra\",\"Prabal Manhas\"]","published":"2025-07-18T09:05:37Z","proceeding":"cs.DB","tasks":"[\"cs.DB\"]","methods":"[\"Graph Neural Network\",\"Reinforcement Learning\"]","has_code":false}