TY - JOUR
T1 - Methodology and Infrastructure for TSN-Based Reproducible Network Experiments
AU - Bosk, Marcin
AU - Rezabek, Filip
AU - Holzinger, Kilian
AU - Marino, Angela Gonzalez
AU - Kane, Abdoul Aziz
AU - Fons, Francesc
AU - Ott, Jorg
AU - Carle, Georg
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Deterministic networking
KW - experiment infrastructure
KW - in-vehicular networking
KW - networking experiments
KW - reproducible experiments
KW - time-sensitive networking
KW - time-sensitive networking methodology
UR - http://www.scopus.com/inward/record.url?scp=85140808974&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2022.3211969
DO - 10.1109/ACCESS.2022.3211969
M3 - Article
AN - SCOPUS:85140808974
SN - 2169-3536
VL - 10
SP - 109203
EP - 109239
JO - IEEE Access
JF - IEEE Access
ER -