AI-Driven Incident Prediction and Self-Healing Infrastructure in Azure Monitor
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I2P125Keywords:
Azure Monitor, Aiops, Predictive Maintenance, Anomaly Detection, Self-Healing Systems, Cloud Automation, Azure Log Analytics, Azure Machine Learning, Azure Automation, Logic Apps, Adaptive Scaling, Incident Management, MTTR ReductionAbstract
Cloud-native environments demand continuous reliability, performance, and proactive incident management. Traditional reactive monitoring approaches often result in delayed resolutions and service disruptions. Microsoft Azure Monitor, combined with AI and automation, enables predictive incident detection, intelligent alert correlation, and self-healing infrastructure. This paper explores the architecture and methodologies for implementing AI-driven operational intelligence in Azure. It highlights how Azure Monitor, Log Analytics, and Azure Automation integrate with machine learning to predict failures and trigger autonomous remediation workflows. The study also presents real-world use cases demonstrating measurable improvements in system uptime, mean time to recovery (MTTR), and operational efficiency.
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