International Journal of Science, Engineering and TechnologyAuthors: Daniel Harrison, Ryan Cooper, Jacob Turner, Hannah Morgan, Chaitanya Srinivas, Rishi Kumar
Abstract: Modern cloud and enterprise infrastructures operate in highly dynamic environments where system failures, performance degradation, configuration errors, and unexpected operational disruptions can significantly impact service availability and business continuity. To address these challenges, organizations are increasingly adopting self-healing infrastructure models that leverage predictive automation, artificial intelligence, machine learning, and real-time monitoring technologies to detect, diagnose, and recover from failures autonomously. This study presents an evidence mapping analysis of predictive recovery mechanisms used in self-healing cloud and enterprise infrastructures, with the objective of identifying prevailing research trends, technological approaches, implementation strategies, and performance outcomes. The research systematically categorizes existing studies based on predictive analytics techniques, fault detection models, automated remediation frameworks, resilience engineering principles, and infrastructure recovery methodologies. The findings reveal that predictive automation significantly enhances system resilience by enabling proactive failure prevention, reducing downtime, improving fault tolerance, accelerating incident response, and optimizing operational efficiency. Furthermore, the study highlights the growing integration of AIOps, autonomous orchestration, anomaly detection, digital observability platforms, and self-adaptive recovery systems within modern infrastructure ecosystems. The evidence mapping also identifies critical research gaps related to explainable automation, multi-cloud resilience, security-aware recovery mechanisms, and large-scale autonomous infrastructure management. The study concludes that predictive recovery mechanisms are fundamental to the development of resilient, self-managing cloud and enterprise environments and provides a comprehensive foundation for future research and practical implementation of next-generation self-healing infrastructure architectures.
DOI: http://doi.org/10.5281/zenodo.20630675
