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AI Research
arXiv CS.AI·May 21·5 min read
When Web Apps Heal Themselves: A MAPE-K Based Approach to Fault Tolerance and Adaptive Recovery
TL;DR
Modern web applications fail unpredictably in complex, changing runtime environments, making manual recovery costly. Researchers built a self-healing framework using MAPE-K (monitor-analyze-plan-execute over shared knowledge base)—a feedback loop that continuously watches system health, diagnoses faults, plans fixes, and executes recovery—combined with AutoFix mechanisms for adaptive repair.
✦ Why It Matters
Engineers can deploy web apps that diagnose and fix their own failures in real time, reducing downtime and operational overhead.
Key Takeaways
- Deploy MAPE-K feedback loops to enable continuous system monitoring and autonomous corrective action execution.
- Integrate AutoFix mechanisms to automatically generate recovery strategies based on detected fault patterns.
- Maintain shared knowledge bases documenting system models and past solutions for faster adaptive recovery.
- Use design and development research methodology to validate self-healing approaches in production web environments.
Full Summary
Web application reliability faces mounting pressure from increasing system complexity and unpredictable runtime conditions. This work proposes a modular self-healing framework grounded in MAPE-K architecture—a feedback loop where a system continuously monitors its state, analyzes deviations, plans corrective actions, and executes them while maintaining a shared knowledge base of system models and past solutions.
The framework integrates AutoFix-inspired mechanisms, which automatically generate and apply fixes based on detected fault patterns. Developed using design and development research (DDR) methodology, the system enables autonomous fault detection and recovery without manual operator intervention.
By combining continuous monitoring with adaptive planning, the framework allows web applications to self-correct when failures occur, reducing downtime and operational overhead.