Decentralized Latency Models and Information Flow for TSN Shapers - A Tutorial

2026-10-08

AI Summary

This study addresses the reliance of delay computation in Time-Sensitive Networking (TSN) on global knowledge and the poor interoperability among heterogeneous shapers. To overcome these limitations, this work proposes a decentralized delay analysis framework based on local information. By formally defining "delay segments," the framework enables standardized interactions across multiple shapers and provides local pseudocode implementations for algorithms such as Strict Priority and Credit-Based Shaping. Experimental evaluations assess five models in terms of delay, jitter, and flow admission efficiency, validating the suitability of different shapers for specific network topologies and traffic distributions. Ultimately, this research offers an efficient and scalable analytical methodology for heterogeneous scheduling in TSNs.

Abstract

Recent advancements in the fields of industrial automation and in-vehicle communication have also increased the requirements on their underlying networks. Traditional field bus technologies are being replaced by regular Ethernet devices with features from the IEEE Time-Sensitive Networking working group. Their standards are designed to achieve zero congestion loss and deterministic latency bounds, but the specific guarantees can differ depending on the shapers used and how the bounds are calculated. After several contributions to the standardization efforts, this work presents a number of decentralized latency models that can be applied locally on the switches, without requiring global knowledge from the rest of the network. It includes a formal definition of delay segments that allows to incorporate interoperability between different shapers and latency models in the future. It presents pseudo-code suggestions for the latency bounds computation of Strict Priority, Credit-Based Shaping, Asynchronous Traffic Shaping, and Cyclic Queuing and Forwarding, all of which can be applied locally on the devices. Finally, five models were implemented and evaluated with respect to their efficiency in terms of latency, jitter, and number of accepted streams. The results illustrate how different shapers are suited for different networks and traffic distributions.

Problem

• Time-Sensitive Networking
• Decentralized Latency Models
• Traffic Shapers
• Deterministic Latency Bounds
• Industrial Automation

Innovation

• Decentralized Latency Models
• Time-Sensitive Networking
• Traffic Shapers
• Delay Segments
• Deterministic Latency Bounds