Evolution of AliYANG: Model-driven and LLM-assisted Network Configuration Management

Abstract

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.