AI-Automated Root-Cause Analysis for Cloud Incidents

The fast growth of cloud computing has resulted in complex and dynamic infrastructures where incidences may spread across virtualized resources and distributed services and multi-layered dependencies. The conventional rule-based troubleshooting processes cannot process high volume of logs, mixed telemetry, and unreliable interaction of services which leads to slow recovery and more downtimes. The current paper introduces an AI-Based Root-Cause Analysis (RCA) system to cloud incidents, which combines deep learning, causal inference, and knowledge-based reasoning to automatically identify, correlate, and diagnose the root causes of faults throughout a cloud system. The structure capitalizes on the usage of log embeddings, anomaly clustering, inter-service dependency mapping, and graph-based causal modelling in an effort to localize the point of failure with high accuracy. A learning cycle based on reinforcement is continuously used to enhance accuracy of RCA by using real-time feedback based on incident resolutions. The experimental findings on real-world cloud datasets have indicated that there are dramatic reductions in time to diagnose root causes, accuracy of incident classification, and mitigation recommendations, compared to the state-of-the-art methods. The solution proposed will increase resiliency and improve service recovery rates and autonomous cloud operations that are appropriate to large-scale DevSecOps ecosystems.