Modern distributed computing environments support critical digital services but frequently encounter operational instability caused by complex interdependencies, infrastructure failures, and delayed incident response. These challenges highlight the need for intelligent infrastructure systems capable of identifying anomalies early and initiating automated corrective actions without human intervention. This study investigates the development of an autonomous self healing infrastructure framework that integrates predictive monitoring with intelligent automation to strengthen reliability, resilience, and operational continuity across distributed computing platforms. The research addresses the problem of reactive infrastructure management by proposing a proactive model that continuously analyzes operational telemetry, predicts potential system failures, and triggers automated remediation workflows. A mixed methodological approach is adopted, combining quantitative analysis of system performance metrics with qualitative evaluation of automation effectiveness in simulated distributed infrastructure environments. Predictive models analyze infrastructure signals such as resource utilization patterns, system logs, and service latency to detect early indicators of degradation, while automation components coordinate corrective responses including resource reconfiguration, service restart, and workload redistribution. Experimental observations indicate that the proposed framework significantly reduces incident response time, improves system availability, and enhances infrastructure stability during abnormal operating conditions. The findings demonstrate the strategic value of predictive automation in enabling autonomous infrastructure operations and minimizing manual intervention. This research contributes to the advancement of resilient infrastructure engineering by providing a scalable framework that supports proactive infrastructure management and strengthens reliability across complex distributed computing ecosystems.
Shekar Vollem (Mon,) studied this question.