PreConfig: A Unified Language Model Framework for Network Configuration Automation

Abstract

Manual network configuration tools are constrained by their reliance on extensive domain expertise and rigid, single-purpose designs, limiting their adaptability to diverse scenarios and complex applications. This paper introduces PreConfig, a novel language model-based framework for automating network configuration tasks. By framing tasks such as configuration generation, translation, analysis, and completion as text-to-text transformations, PreConfig unifies these processes under a single versatile model. Leveraging advancements in natural language processing, PreConfig eliminates the need for extensive manual re-engineering by automatically learning domain-specific patterns through continued training on a specialized network configuration corpus. To address the lack of domain knowledge in general language models, we construct a comprehensive dataset from vendor manuals and community forums and fine-tune a programming language model for robust performance across various tasks. Additionally, we propose ConfigBLEU, a novel evaluation metric that incorporates syntax-aware features to assess the accuracy of generated configurations. Experimental results demonstrate that PreConfig significantly outperforms existing tools and general-purpose language models in both syntactic accuracy and semantic correctness across diverse network configuration tasks. This work establishes a unified and adaptable approach for advancing network configuration automation.