Observability and AIOps in Cloud-Scale DevOps

Observability is fundamental for operating complex, dynamic, and distributed cloud-native systems at scale. With the rise of microservices and CloudOps, massive volumes of telemetry data overwhelm manual methods. Unlike prior surveys, this study introduces an Adaptive Observability–AIOps Integration Model (AOIM) that formalizes dynamic feedback between telemetry streams and AI-driven analytics. This framework is validated through two enterprise-scale case studies, providing statistically significant improvements in MTTD, MTTR, and false positive reduction. Artificial Intelligence for IT Operations (AIOps) leverages machine learning to automate the detection, analysis, and remediation in DevOps, enabling real-time actionable insights. This paper presents a comprehensive review of observability and AIOps for cloud-scale DevOps, detailing their principles, architectures, technical patterns, challenges, and practical implementations. We survey the latest research and industrial adoption, propose a reference architecture, analyze quantitative and qualitative case study findings, and outline critical future research opportunities.