Flow-Rule Generation for SDN Using LLMs with Retry-Based Deployment Validation

This work proposes a pipeline for Software Defined Networking (SDN) that enables natural-language-based flow rule configuration using large language models (LLMs). The system addresses two key challenges: 1) the ambiguity and incompleteness of natural language inputs, and 2) the difficulty of reliably translating them into deployable SDN configurations. To this end, the pipeline integrates: i) an intent recognition module that refines user prompts via iterative clarification, and ii) a retrybased correction mechanism that handles failed configurations by regenerating and resubmitting corrected versions. These components are combined with intermediate YAML generation, documentation-based enrichment, and final translation into OpenFlow-compliant JSON for Ryu controllers. The pipeline is evaluated on flow rule deployment tasks of varying complexity, achieving an accuracy up to 96.7%, while maintaining costefficiency with an estimated API cost of only 0.08 per 100 configurations and remaining model model-agnostic.