Reproducing and extending research on TimeSensitive Networking (TSN) scheduling has become increasingly challenging, as most published methods lack open-source implementations. The few available implementations are often scattered across different programming languages and formats, forcing researchers to reimplement algorithms from scratch—a time-consuming and error-prone process that hinders fair comparison of methods and slows research progress. In this work, we present TSNKit, an open-source toolkit designed to address these challenges through: (i) standardized implementations of a broad set of representative scheduling algorithms with unified interfaces for integrating new methods; (ii) an end-to-end pipeline covering test case generation, scheduling, and simulation-based validation; and (iii) comprehensive benchmarking modules for reproducible performance evaluation. TSNKit enables researchers to reproduce published results, extend existing methods, and perform fair comparisons across algorithms. Our ongoing work extends TSNKit to support multiple traffic shapers beyond Time-Aware Shaping (TAS), improve benchmark efficiency through enhanced scheduling heuristics, and incorporate hardware-in-the-loop capabilities for seamless real-world deployment.

A growing trend in large-scale industrial system design is the integration of multiple real-time, mission-critical applications over shared network infrastructures to reduce hardware costs and improve scalability. Recent advances in network resource partitioning techniques provide practical mechanisms for managing these applications hierarchically while maintaining operational isolation. However, as system complexity increases, applications often exhibit multi-state behaviors that challenge the system's ability to meet stringent timing requirements - especially under static resource partitions. While dynamic resource reconfiguration can restore feasibility, it is typically costly and disruptive in industrial environments. To address this challenge, we propose a flexibility-aware network resource partitioning framework that introduces a novel metric - partition flexibility - to quantify how effectively a resource partition supports an application's state transitions. Using this metric, we develop efficient strategies for both static partition allocation and dynamic partition adjustment, with the goal of minimizing reconfiguration overhead. We validate our framework design through a real-world case study involving a NASA extra-terrestrial habitat system deployed on a time-sensitive networking (TSN) testbed. Extensive simulations further demonstrate that the proposed partitioning framework reduces$\mathbf{5 6. 4 \%}$reconfigurations compared to the state-of-the-art methods.

Time-Sensitive Networking (TSN) technology has been increasingly deployed in mission- and safety-critical industrial applications to achieve high throughput and deterministic communications. To provide stringent timing guarantee, TSN requires that network devices follow a predefined communication schedule for real-time end-to-end packet processing, involving both TSN bridges and end stations. Extensive efforts have been devoted on the TSN bridge design in the literature. Achieving TSN compatibility on the end stations (especially on COTS hardware), however is challenging due to their constrained resources. To fill this gap, this work presents a software-based open-source TSN end station design that i) enables ultra-low latency and nanosecond-level transmission accuracy based on DPDK, and ii) employs a novel multi-core scheduling framework to boost the throughput of real-time TSN traffic. Our proposed solution leverages existing COTS hardware and thus is more generic and cost-effective compared to existing hardware-centric solutions. We validate our design by developing a prototype end station and incorporating it in a real-world TSN testbed. Our extensive experiments demonstrate the efficiency and effectiveness of our design compared with other state-of-the-art solutions.

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 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 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.