PreConfig: A Pretrained Model for Automating Network Configuration
Fuliang Li, Haozhi Lang, Jiajie Zhang, Jiaxing Shen, Xingwei Wang

TL;DR
PreConfig is a pretrained language model-based tool that automates network configuration tasks, improving versatility and performance over traditional manual and rule-based methods by framing tasks as text transformations.
Contribution
The paper introduces PreConfig, a novel pretrained model that unifies various network configuration tasks and overcomes existing limitations through specialized pretraining and data mining techniques.
Findings
PreConfig outperforms traditional tools in complex networking environments.
The model effectively handles configuration generation, translation, and analysis.
PreConfig demonstrates robustness and versatility across diverse network scenarios.
Abstract
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…
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Taxonomy
TopicsSoftware System Performance and Reliability · Service-Oriented Architecture and Web Services
