Methodology and Infrastructure for TSN-Based Reproducible Network Experiments

• Published in: IEEE access Vol. 10; pp. 109203 - 109239
• Main Authors: Bosk, Marcin, Rezabek, Filip, Holzinger, Kilian, Marino, Angela Gonzalez, Kane, Abdoul Aziz, Fons, Francesc, Ott, Jorg, Carle, Georg
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automotive engines; Configurations; Deterministic networking; Domains; Ethernet; experiment infrastructure; in-vehicular networking; Infrastructure; Jitter; Methodology; Network latency; networking experiments; Packet loss; Performance evaluation; Real time; Real-time systems; reproducible experiments; Scalability; Smart manufacturing; Synchronization; time-sensitive networking; time-sensitive networking methodology; Timing; Topology; Traffic control; Vehicles; Workflow
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3211969

Time-Sensitive Networking (TSN) is a set of standards offering bounded latency and jitter, low packet loss, and reliability for Ethernet-based systems while allowing best-effort and real-time traffic to coexist. Domains that use TSN include intra-vehicular networks (IVNs), aerospace, professional audio-video solutions, and smart manufacturing. All these areas shift towards Ethernet due to its scalability, throughput, easy to develop applications, and affordability to produce in a large scale. In this work, we devise a methodology that introduces a workflow comprising several steps to assess TSN in various domains. The first step defines requirements and assesses which real-time traffic is present within a given domain. The second step focuses on configuration of a representative TSN-based network. The step proceeds with performance evaluation of different TSN standards in the chosen configuration(s). The third - optional - step supports optimizing the system to fulfill the identified requirements. The methodology is generalized by assessing the various TSN domains and finding their commonalities. As a result, we see the methodology can be applied to other TSN solutions. We provide a detailed case study for the domain of IVNs, from which the methodology is derived. We summarize the key requirements, systematically analyze IVNs traffic patterns for real-time and best effort traffic, and evaluate the performance of crucial TSN standards recommended by the IEEE 802.1DG Automotive Profile. The methodology builds on top of infrastructure framework, EnGINE, that offers an environment for reproducible and scalable TSN experiments and relies on commercial off the shelf hardware and open-source solutions. The framework allows to evaluate various standards and identify suitable topologies with focus on Layer 2 solutions. Using EnGINE, we evaluated the various traffic patterns and their corresponding TSN configurations and identified if and how the IVN requirements can be fulfilled.