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Ahead of Print article ยท Journal article

Improving Latency Analysis for Flexible Window-Based GCL Scheduling in TSN Networks by Integration of Consecutive Nodes Offsets

From

Department of Applied Mathematics and Computer Science, Technical University of Denmark1

Embedded Systems Engineering, Department of Applied Mathematics and Computer Science, Technical University of Denmark2

Beihang University3

Time-Sensitive Networking (TSN) is an upcoming set of Ethernet standards designed for real-time and safety-critical Internet of Things (IoT) applications in automotive, aerospace and industrial automation domains. With the combination, complexity and flexibility of flow control mechanisms in TSN connected systems, the performance analysis for mixed-critical messages is becoming a difficult challenge.

The flexible window-based Gate Control List (GCL) scheduling model has been proposed as a relaxation to assumptions on frames-to-window allocation, mutually exclusive gates opening, and scheduled end systems and switches, which offers more flexibility in the configuration of GCLs. In this paper, we are interested in providing a reliable verification method based on the network calculus theory to drive GCL configurations for TSN networks.

To the best of our knowledge, this is the first performance analysis method suitable for the general flexible window-based GCLs in entire TSN networks, by reflecting the relative positional relationships of windows for same priority queues on consecutive nodes and constructing the window limitations into the shaper curve, in order to reduce the pessimism of the latency bounds.

We validate the proposed method through Industrial IoT synthetics test cases and two large realistic cases, showing the significant reduction in pessimism on delay bounds, and the correctness and scalability by comparing with results from the previous work and simulation results.

Language: English
Publisher: IEEE
Year: 2021
Pages: 5574-5584
ISSN: 23722541 and 23274662
Types: Ahead of Print article and Journal article
DOI: 10.1109/JIOT.2020.3031932
ORCIDs: Zhao, Luxi , Pop, Paul and 0000-0002-1173-8014

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