Machine Learning-Based Fault Prediction and Recovery in Distributed Systems

  • Authors

    • Dr. Anita Verma Associate Professor, Banaras Hindu University, India. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2025PIIB2H6U

    Published 10-05-2025

  • Distributed Systems, Fault Prediction, Fault Recovery, Machine Learning, Deep Learning, Reinforcement Learning, Predictive Maintenance, Self-Healing Systems, Cloud Computing, Anomaly Detection

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    A. Verma, “Machine Learning-Based Fault Prediction and Recovery in Distributed Systems”, IJADSMC, vol. 8, no. 2, pp. 01–15, Oct. 2025, doi: 10.67228/30713498/IJADSMC-2025PIIB2H6U.

  • Abstract

    Distributed systems play a vital role in modern cloud computing, edge computing, IoT, and enterprise applications by providing scalable and reliable services. However, these systems are vulnerable to failures caused by hardware faults, software defects, network issues, resource limitations, and workload fluctuations. Traditional fault management approaches, which rely on rule-based monitoring and reactive recovery, are often inadequate for handling the complexity of large-scale distributed environments. This study presents a Machine Learning (ML)-based Fault Prediction and Recovery Framework that enables proactive fault management through the analysis of system logs, performance metrics, resource utilization data, and network telemetry. The framework integrates data collection, feature engineering, fault prediction, anomaly detection, and autonomous recovery mechanisms. Supervised learning models classify known faults, while unsupervised learning techniques detect unknown anomalies. Reinforcement learning agents further optimize recovery actions through continuous interaction with the system environment. Experimental results demonstrate that the proposed framework achieves fault prediction accuracy above 95%, reduces downtime by more than 40%, lowers Mean Time to Recovery (MTTR), and improves overall system availability. The findings highlight the effectiveness of machine learning in enabling self-healing distributed systems and resilient cloud-native applications. Future research will focus on federated learning, explainable AI, and edge-cloud integration for enhanced intelligent fault management.

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