Reinforcement Learning based Self-Healing Logistics Databases for High-Availability Disaster Response Operations
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Abstract
Disaster response systems are based on the ability to generate access to constant and reliable information; however, logistics database malfunctions can lead to severe losses. This study introduces a self-healing system that utilizes reinforcement learning (RL) to ensure high-availability and reduce downtime in critical environments. The data used in this study were obtained from the U.S. Coast Guard MISLE reports and New York Spill Incident Record, in real-world data, to develop a pipeline based on PostgreSQL and anomaly labeling automation. RL simulations in a custom environment that simulates an actual time database behavior and an agent trained by Deep Q-Network (DQN) that learns to approach the anomalies by considering five corrective actions including imputation, eliminating duplication, rollback, query optimization, and plan reconfiguration. The system was tested and monitored in a streaming scenario using the Mean Time to Recovery (MTTR), Downtime Success Rate (DSR), and anomaly resolution accuracy. The results demonstrate an accuracy of 90.1% ± 2.3%, an uptime of 84.4% ± 3.1%, and an MTTR of 1.14 ± 0.29 s in simulated environments. This proves that the RL guide effectively and quickly resolves issues while underscoring the necessity for real-world validation. The proposed design can open an auto-scalable journey to self-sustaining database health in mission-critical applications with reduced human monitoring and manual adjustment
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Publication Details
- DOI: 10.1109/STI69347.2025.11367593
- Type of Publication:
- Conference Name: 2025 IEEE 7th International Conference on Sustainable Technologies for Industry 5.0 (STI)
- Date of Conference: 12/11/2025 - 12/11/2025
- Venue: Dhaka
- Organizer: Green University of Bangladesh