Autonomous Self-Adaptation in the Cloud: ML-Heal’s Framework for Proactive Fault Detection and Recovery
Qais Al-Na’amneh, Mahmoud Mohammad Aljawarneh, Rahaf Hazaymih, Ayoub Alsarhan, Khalid Hamad Alnafisah, Nayef H. Alshammari, Sami Aziz Alshammari · International Journal of Advanced Computer Science and Applications · 2025
Cloud computing environments increasingly host applications constructed from orchestrated service compositions, which deliver enhanced functionality through distributed work-flows. This paradigm, however, introduces vulnerabilities where component failures can cascade, disrupting entire applications. Conventional fault tolerance often falls short in these dynamic settings. This paper introduces ML-Heal, an autonomous self-healing framework architected to bolster the resilience of such service compositions. ML-Heal leverages machine learning for proactive failure detection, precise diagnosis, and intelligent recovery strategy selection. The framework integrates real-time monitoring data, applies ML-based anomaly detection and classification to identify faults, and plans corrective actions via a learned policy or predictive models. Implemented using Python with scikit-learn models and a custom orchestration layer, its efficacy is demonstrated through simulated fault injection scenarios. Illustrative system architecture and evaluation results show that this ML-driven methodology significantly curtails recovery time and augments availability when confronted with faults, showcasing AI’s potential in creating more robust, self-adaptive cloud service compositions with minimal human oversight.