Autonomous Self-Adaptation in the Cloud: ML-Heal’s Framework for Proactive Fault Detection and Recovery

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.

@article{Al-Na’amneh2025,
  title     = {Autonomous Self-Adaptation in the Cloud: ML-Heal’s Framework for Proactive Fault Detection and Recovery},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Qais Al-Na’amneh and Mahmoud Aljawarneh and Rahaf Hazaymih and Ayoub Alsarhan and Khalid Hamad Alnafisah and Nayef H. Alshammari and Sami Aziz Alshammari},
  doi       = {10.14569/IJACSA.2025.0160892},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160892}
}