Introducing Large Language Models in the Design Flow of Time Sensitive Networking

2025-09-30

AI Summary

This paper addresses the challenges of high configuration complexity and excessive human dependency in end-to-end Time-Sensitive Networking (TSN) orchestration, which hinder its adoption in real-time safety-critical systems. To this end, we propose the first Large Language Model (LLM)-assisted automated TSN orchestration framework. Our method integrates an LLM with a Network Calculus (NC) engine, network simulation tools, and external verification modules to enable closed-loop generation—from natural-language requirements to formally verifiable TSN configurations. Key contributions include: (1) pioneering the application of LLMs to deterministic networking orchestration; (2) designing an extensible, TSN-specific architectural blueprint and implementation roadmap; (3) empirically validating the feasibility of current LLMs for TSN configuration tasks; and (4) identifying critical future directions—including TSN-specific datasets and benchmark suites—thereby bridging a fundamental gap in LLM applications to deterministic networks.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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, 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, referred to as TSN orchestration. This paper outlines the steps involved in TSN orchestration and the associated challenges. To assess the capabilities of existing LLM models, we conduct an initial proof-of-concept case study focused on TSN configuration 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. Finally, we highlight key challenges and research directions, including the development of TSN-focused datasets, standardized benchmark suites, and the integration of external tools such as Network Calculus (NC) engines and simulators. This work provides the first roadmap toward assessing the feasibility of LLM-assisted TSN orchestration.

Problem

• Automating TSN orchestration using Large Language Models
• Addressing complexity in Time Sensitive Networking configuration
• Developing LLM framework for end-to-end TSN deployment

Innovation

• LLM-assisted framework automates TSN orchestration
• Multi-model case study evaluates TSN configuration capabilities
• Integration of Network Calculus engines enhances deployment