Configuration management in large-scale cloud networks is increasingly challenging due to vendor heterogeneity, diverse configuration interfaces, and rapid configuration evolution across the network life cycle. Existing approaches rely heavily on vendor- and interface-specific templates and scripts, which are difficult to validate, costly to maintain, and scale poorly. We introduce AliYANG, a YANG-based configuration modeling framework that unifies configuration representation across vendors and management interfaces. It extends YANG to capture CLI semantics and derives a vendor-agnostic core model that separates configuration semantics from vendor-specific implementations. We further present NetCMDB, the production software infrastructure for AliYANG, which compiles models into typed configuration objects and supports end-to-end, model-driven configuration workflows. As networks evolve, manually constructing and maintaining models becomes a bottleneck. We incorporate LLM-assisted automation to facilitate vendor model augmentation, core model design, and bidirectional translation code generation. We report our three-year production deployment experience managing hundreds of thousands of devices, present evaluation results and case studies, and share lessons from operating a model-driven configuration system at cloud scale.

Root causing and failure localization are critical to maintain reliability in cloud network operations. When an incident is reported, network operators must review massive volumes of monitoring data and identify the root cause (i.e., error device) as fast as possible, making it extremely challenging even for experienced operators. Large language models (LLMs) have shown great potential in text understanding and reasoning. In this paper, we present BiAn, an LLM-based framework designed to assist operators in efficient incident investigation. BiAn processes monitoring data and generates error device rankings with detailed explanations. To date, BiAn has been deployed in our network infrastructure for 10 months and it has successfully assisted operators in identifying error devices more quickly, reducing time to root causing by 20.5% (55.2% for high-risk incidents). Extensive performance evaluations based on 17 months of real cases further demonstrate that BiAn achieves accurate and fast failure localization. It improves accuracy by 9.2% compared to the baseline approach.