Real-Time Scheduling for 802.1Qbv Time-Sensitive Networking (TSN): A Systematic Review and Experimental Study

TL;DR

This paper systematically reviews TAS-based TSN scheduling methods and validates them through a combined real-testbed and simulation study. It classifies methods into fixed-routing and joint-routing frameworks, with no-wait and wait-allowed delay models, and evaluates 17 representative approaches across diverse industrial scenarios. The work reveals that no single method dominates across all settings, highlighting trade-offs between schedulability, scalability, and schedule quality, and emphasizes the importance of fair, multi-metric evaluation and realistic testbeds. The findings provide benchmarks and actionable guidance for researchers designing next-generation TSN schedulers and for practitioners deploying deterministic TSN networks in industry.

Abstract

Time-Sensitive Networking (TSN) has been recognized as one of the key enabling technologies for Industry 4.0 and has been deployed in many mission- and safety-critical applications e.g., automotive and aerospace systems. Given the stringent real-time requirements of these applications, the Time-Aware Shaper (TAS) draws special attention among TSN's many traffic shapers due to its ability to achieve deterministic timing guarantees. Many scheduling methods for TAS shapers have been recently developed that claim to improve system schedulability. However, these scheduling methods have yet to be thoroughly evaluated, especially through experimental comparisons, to provide a systematical understanding of their performance using different evaluation metrics in diverse application scenarios. In this paper, we fill this gap by presenting a systematic review and experimental study on existing TAS-based scheduling methods for TSN. We first categorize the system models employed in these works along with the specific problems they aim to solve, and outline the fundamental considerations in the designs of TAS-based scheduling methods. We then perform an extensive evaluation on 17 representative solutions using both high-fidelity simulations and a real-life TSN testbed, and compare their performance under both synthetic scenarios and real-life industrial use cases. Through these experimental studies, we identify the limitations of individual scheduling methods and highlight several important findings. We expect this work will provide foundational knowledge and performance benchmarks needed for future studies on real-time TSN scheduling.

Figures (19)

• Figure 1: An illustration of the Time-Aware Shaper (TAS) mechanism in a Time-Sensitive Networking (TSN) bridge.
• Figure 2: An example TSN network comprising 5 bridges and 5 end stations (ES) with $4$ streams deployed in the network.
• Figure 3: An illustration of various scheduling models applied on the example in Fig. \ref{['fig:bridge_net']}. $f_{i,k,a,b}$ indicates the $k$-th frame of stream-$i$ scheduled on link $(a, b)$, $f_{i,k,a,b}^{r}$ indicates the $r$-th fragment if fragmentation or preemption is applied. To simplify the notations, we use $t$ to denote the talker and $l$ to denote the listener associated with each path. The up arrow indicates the frame release time at the talker and the down arrow indicates the frame reception time at the listener. Solid area indicates the actual transmission pattern and shallow area indicates the traffic planning result. The dash line indicates the border of a GCL entry.
• Figure 4: Classification of the TSN real-time scheduling methods based on the employed system models.
• Figure 5: An example of different formalization approaches for no-overlap constraints.