Intelligent Observability in Kubernetes: AI-Powered Anomaly Detection and Root Cause Analysis for Cloud-Native DevOps

Abstract

The rapid adoption of Kubernetes for cloud-native applications has led to increased complexity in system monitoring, anomaly detection, and root cause analysis. Traditional observability tools struggle to handle the vast amounts of telemetry data generated by microservices-based architectures, resulting in delayed incident detection and resolution. This research explores AI-powered observability in Kubernetes environments, leveraging machine learning and deep learning techniques for real-time anomaly detection and automated root cause analysis. The study investigates the integration of AI models for log analysis, metric correlation, and distributed tracing, demonstrating significant improvements in incident response times, accuracy, and resource efficiency. A comparative analysis of AI-driven observability tools versus conventional monitoring solutions is presented, showcasing the advantages of AI in cloud-native DevOps workflows. Experimental results validate the effectiveness of AI-powered observability in reducing system downtime, enhancing performance monitoring, and improving operational resilience. The findings contribute to the evolution of intelligent DevOps practices, bridging AI with cloud-native observability for automated anomaly detection and efficient root cause identification.