Manual network configuration automation (NCA) tools face significant challenges in versatility and flexibility due to their reliance on extensive domain expertise and manual design, limiting their adaptability to diverse scenarios and complex application needs. This paper introduces PreConfig, an innovative NCA tool that leverages a pretrained language model for automating network configuration tasks. PreConfig is designed to address the complexity and variety of NCA tasks by framing them as text-to-text transformation problems, thus unifying the tasks of configuration generation, translation, and analysis under a single, versatile model. Our approach overcomes existing tools' limitations by utilizing advances in natural language processing to automatically comprehend and generate network configurations without extensive manual re-engineering. We confront the challenges of integrating domain-specific knowledge into pretrained models and the scarcity of supervision data in the network configuration field. Our solution involves constructing a specialized corpus and further pretraining on network configuration data, coupled with a novel data mining technique for generating task supervision data. The proposed model demonstrates robustness in configuration generation, translation, and analysis, outperforming conventional tools in handling complex networking environments. The experimental results validate the effectiveness of PreConfig, establishing a new direction for automating network configuration tasks with pretrained language models.

翻译：手动网络配置自动化（NCA）工具因依赖广泛的领域专业知识和人工设计，在通用性和灵活性方面面临显著挑战，限制了其对多样化场景和复杂应用需求的适应能力。本文提出PreConfig——一种创新的NCA工具，它利用预训练语言模型自动完成网络配置任务。PreConfig旨在应对NCA任务的复杂性和多样性，通过将其转化为文本到文本的转换问题，从而在单一通用模型框架下统一配置生成、转换和分析任务。我们的方法克服了现有工具的局限性，利用自然语言处理领域的进展自动理解和生成网络配置，无需大量手动重工程。我们解决了将领域特定知识融入预训练模型以及网络配置领域监督数据稀缺的挑战。解决方案包括构建专门语料库并对网络配置数据进行进一步预训练，同时结合一种用于生成任务监督数据的新型数据挖掘技术。所提出的模型在配置生成、转换和分析中展现出鲁棒性，在处理复杂网络环境时优于传统工具。实验结果验证了PreConfig的有效性，为利用预训练语言模型自动化网络配置任务开辟了新方向。