Intelligent Cloud Monitoring Using AIOps in the Microsoft Azure Ecosystem

Abstract

The growing complexity of cloud-native applications has made conventional monitoring techniques ineffective for ensuring system performance and reliability. This research explores the use of Artificial Intelligence for IT Operations (AIOps) in the Microsoft Azure environment to achieve intelligent, automated and predictive cloud monitoring. Our system leverages Azure Monitor, Log Analytics and Azure Machine Learning to ingest and analyses extensive telemetry data, such as logs, metrics and distributed traces. An integrated machine learning framework, which combines unsupervised anomaly detection (such as clustering and statistical thresholding) with supervised learning models is used to detect anomalies in real time. The framework is validated with cloud workload datasets from cloud-native microservices deployed in the Azure Kubernetes Service (AKS). Self-healing mechanisms through automation workflows using Azure Automation and Logic Apps help automate responses. The results of the experimental studies show that the proposed AIOps approach enhances anomaly detection rates up to 92% and lowers mean time to resolution (MTTR) by around 30-40% when compared to traditional monitoring systems. The framework is scalable, flexible and cost-effective in cloud ecosystems. This study demonstrates the value of combining AI analytics with cloud-based observability tools to shift IT operations from reactive to proactive and self-healing modes.