Introducing Large Language Models into the Design Flow of Time-Sensitive Networking

Abstract

The growing demand for real-time, safety-critical systems has significantly increased both the adoption and complexity of Time-Sensitive Networking (TSN). Configuring an optimized TSN network is highly challenging, requiring careful planning, design, analysis, verification, validation, and deployment. Large Language Models (LLMs) have recently demonstrated strong capabilities in solving complex tasks, positioning them as promising candidates for automating end-to-end TSN deployment and management, referred to as TSN orchestration. This paper outlines the steps involved in TSN orchestration and the associated challenges. To assess the capabilities of existing LLMs, we conduct an initial proof-of-concept case study focused on TSN tasks across multiple models. Building on these insights, we propose an LLM-assisted orchestration framework. Unlike prior research on LLMs in computer networks, which has concentrated on general configuration and management, TSN-specific orchestration has not yet been investigated. We present the building blocks for automating TSN using LLMs, describe the proposed pipeline, and analyze opportunities and limitations for real-world deployment. This work provides the first roadmap toward assessing the feasibility of LLM-assisted TSN orchestration.