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

Rubi Debnath, Luxi Zhao, Mohammadreza Barzegaran, Sebastian Steinhorst

TL;DR

This paper addresses configuring Time-Sensitive Networking ($TSN$) for safety-critical systems, where end-to-end deployment is labor-intensive and requires domain expertise. It proposes LLM-assisted TSN orchestration, a pipeline where an $LLM$ parses inputs, designs architecture, assigns traffic types and priorities, synthesizes network configurations, conducts schedulability analysis with external tools, generates OMNeT++ deployment files, runs simulations, and performs continuous monitoring. The approach emphasizes hybrid integration with Network Calculus engines and simulation frameworks to preserve deterministic guarantees while reducing manual effort. A preliminary case study compares multiple off-the-shelf LLMs on TSN-oriented tasks, identifying capabilities and limitations and highlighting the need for TSN-focused datasets and customized tools. The paper contributes a practical roadmap and identifies open research directions toward open datasets, benchmarks, model fine-tuning, and hybrid orchestration to enable scalable TSN deployment.

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.

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

TL;DR

This paper addresses configuring Time-Sensitive Networking () for safety-critical systems, where end-to-end deployment is labor-intensive and requires domain expertise. It proposes LLM-assisted TSN orchestration, a pipeline where an parses inputs, designs architecture, assigns traffic types and priorities, synthesizes network configurations, conducts schedulability analysis with external tools, generates OMNeT++ deployment files, runs simulations, and performs continuous monitoring. The approach emphasizes hybrid integration with Network Calculus engines and simulation frameworks to preserve deterministic guarantees while reducing manual effort. A preliminary case study compares multiple off-the-shelf LLMs on TSN-oriented tasks, identifying capabilities and limitations and highlighting the need for TSN-focused datasets and customized tools. The paper contributes a practical roadmap and identifies open research directions toward open datasets, benchmarks, model fine-tuning, and hybrid orchestration to enable scalable TSN deployment.

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.

Paper Structure

This paper contains 28 sections, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Motivation
  3. Scope and Contributions
  4. Background
  5. Time-Sensitive Networking (TSN)
  6. Large Language Models in Networking
  7. Research Questions
  8. End-to-End TSN Orchestration using Large Language Models
  9. Input Parsing
  10. Architecture Design
  11. Traffic Type and Priority Assignment
  12. Network Configuration
  13. Schedulability Analysis
  14. OMNeT++ Configuration File Generation
  15. Simulation-based Performance Evaluation
  16. ...and 13 more sections

Figures (6)

  • Figure 1: Typical steps involved in the overall TSN orchestration for end-to-end deployment.
  • Figure 2: Overview of the case study setup.
  • Figure 3: Proposed LLM-aided TSN Orchestrator architecture. The pipeline illustrates the key steps in the end-to-end orchestration and deployment of TSN networks.
  • Figure 4: Simple
  • Figure 5: ERG
  • ...and 1 more figures