Towards Distributed Flow Scheduling in IEEE 802.1Qbv Time-Sensitive Networks
Authors: 
Miao Guo, Shibo He, 
Chaojie Gu, Xiuzhen Guo, Jiming Chen, Tao Gao, Tongtong WangAuthors Info & Claims
Miao Guo, Shibo He, Chaojie Gu, Xiuzhen Guo, Jiming Chen, Tao Gao, Tongtong WangAuthors Info & ClaimsArticle No.: 104, Pages 1 - 30
Abstract
Flow scheduling plays a pivotal role in enabling Time-Sensitive Networking (TSN) applications. Current flow scheduling mainly adopts a centralized scheme, posing challenges in adapting to dynamic network conditions and scaling up for larger networks. To address these challenges, we first thoroughly analyze the flow scheduling problem and find the inherent locality nature of time scheduling tasks. Leveraging this insight, we introduce the first distributed framework for IEEE 802.1Qbv TSN flow scheduling. In this framework, we further propose a multi-agent flow scheduling method by designing Deep Reinforcement Learning (DRL)-based route and time agents for route and time planning tasks. The time agents are deployed on field devices to schedule flows in a distributed way. Evaluations in dynamic scenarios validate the effectiveness and scalability of our proposed method. It enhances the scheduling success rate by 20.31% compared to state-of-the-art methods and achieves substantial cost savings, reducing transmission costs by 410× in large-scale networks. Additionally, we validate our approach on edge devices and a TSN testbed, highlighting its lightweight nature and ease of deployment.
References
[1]
Ayman A. Atallah, Ghaith Bany Hamad, and Otmane Ait Mohamed. 2019. Routing and scheduling of time-triggered traffic in time-sensitive networks. IEEE Trans. Industr. Inform. 16, 7 (2019), 4525–4534.
[2]
Zongrong Cheng, Dong Yang, Weiting Zhang, Jie Ren, Hongchao Wang, and Hongke Zhang. 2022. DeepCQF: Making CQF scheduling more intelligent and practicable. In Proceedings of the IEEE International Conference on Communications (ICC’22). 1–6. DOI:
[3]
Hao Ran Chi, Maria de Fátima Domingues, Konstantin. I. Kostromitin, Ahmad Almogren, and Ayman Radwan. 2021. Spatiotemporal D2D small cell allocation and on-demand deployment for microgrids. IEEE Access 9 (2021), 116830–116844. DOI:
[4]
Sandeep Chinchali, Pan Hu, Tianshu Chu, Manu Sharma, Manu Bansal, Rakesh Misra, Marco Pavone, and Sachin Katti. 2018. Cellular network traffic scheduling with deep reinforcement learning. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence and 13th Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence (AAAI’18/IAAI’18/EAAI’18). New Orleans, Louisiana, USA. AAAI Press, Article 94, 9 pages.
[5]
Silviu S. Craciunas and Ramon Serna Oliver. 2016. Combined task-and network-level scheduling for distributed time-triggered systems. Real-Time Syst. 52 (2016), 161–200.
[6]
Silviu S. Craciunas, Ramon Serna Oliver, Martin Chmelík, and Wilfried Steiner. 2016. Scheduling real-time communication in IEEE 802.1Qbv time sensitive networks. In Proceedings of the 24th International Conference on Real-time Networks and Systems. 183–192.
[7]
Frank Dürr and Naresh Ganesh Nayak. 2016. No-wait packet scheduling for IEEE time-sensitive networks (TSN). In Proceedings of the 24th International Conference on Real-time Networks and Systems. 203–212.
[8]
Jonathan Falk, Frank Dürr, and Kurt Rothermel. 2018. Exploring practical limitations of joint routing and scheduling for TSN with ILP. In Proceedings of the IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA’18). IEEE, 136–146.
[9]
Bernard Fong, A. C. M. Fong, and Kim-Fung Tsang. 2021. Capacity and link budget management for low-altitude telemedicine drone network design and implementation. IEEE Commun. Stand. Mag. 5, 4 (2021), 74–78. DOI:
[10]
OPC Foundation. 2023. OPC UA Specification Part 14. Retrieved from https://reference.opcfoundation.org/Core/Part14/v105/docs/7.3.3
[11]
Voica Gavriluţ and Paul Pop. 2018. Scheduling in time sensitive networks (TSN) for mixed-criticality industrial applications. In Proceedings of the 14th IEEE International Workshop on Factory Communication Systems (WFCS’18). IEEE, 1–4.
[12]
OSPF Working Group. 2008. Open Shortest Path First. Retrieved from https://en.wikipedia.org/wiki/Open_Shortest_Path_First
[13]
TSN Group. 2015. IEEE 802.1Qcc Standard. Retrieved from https://www.ieee802.org/1/files/public/docs2015/cc-cummings-topology-discovery-v1.pdf
[14]
TSN Group. 2016. IEEE 802.1Qbv Standard. Retrieved from https://www.ieee802.org/1/pages/802.1bv.html
[15]
Xiaowu He, Xiangwen Zhuge, Fan Dang, Wang Xu, and Zheng Yang. 2023. Deep-scheduler: Enabling flow-aware scheduling in time-sensitive networking. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’23).
[16]
David Hellmanns, Lucas Haug, Moritz Hildebrand, Frank Dürr, Stephan Kehrer, and René Hummen. 2021. How to optimize joint routing and scheduling models for TSN using integer linear programming. In Proceedings of the 29th International Conference on Real-Time Networks and Systems. 100–111.
[17]
Huang Huang and Jingjing Li. 2020. Distributed energy-aware reliable routing and TDMA link scheduling in wireless sensor networks. In Proceedings of the 29th International Conference on Computer Communications and Networks (ICCCN’20). 1–10. DOI:
[18]
Hongyu Jia, Yu Jiang, Chunmeng Zhong, Hai Wan, and Xibin Zhao. 2021. TTDeep: Time-triggered scheduling for real-time ethernet via deep reinforcement learning. In Proceedings of the IEEE Global Communications Conference (GLOBECOM’21). 1–6. DOI:
[19]
Baochun Li, Li Chen, and Xi Peng. 2022. ns.py. Retrieved from https://github.com/TL-System/ns.py
[20]
Yuhan Lin, Xi Jin, Tianyu Zhang, Meiling Han, Nan Guan, and Qingxu Deng. 2021. Queue assignment for fixed-priority real-time flows in time-sensitive networks: Hardness and algorithm. J. Syst. Archit. 116 (2021), 102141. DOI:
[21]
Lucia Lo Bello and Wilfried Steiner. 2019. A perspective on IEEE time-sensitive networking for industrial communication and automation systems. Proc. IEEE 107, 6 (2019), 1094–1120. DOI:
[22]
Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, and Mohammad Alizadeh. 2019. Learning scheduling algorithms for data processing clusters. In Proceedings of the ACM Special Interest Group on Data Communication. 270–288.
[23]
John L. Messenger. 2018. Time-sensitive networking: An introduction. IEEE Commun. Stand. Mag. 2, 2 (2018), 29–33. DOI:
[24]
Sanaz Mohammadi, Didier Colle, and Wouter Tavernier. 2022. Latency-aware topology discovery in SDN-based time-sensitive networks. In Proceedings of the IEEE 8th International Conference on Network Softwarization (NetSoft’22). 145–150. DOI:
[25]
Ramon Serna Oliver, Silviu S. Craciunas, and Wilfried Steiner. 2018. IEEE 802.1Qbv gate control list synthesis using array theory encoding. In Proceedings of the IEEE Real-time and Embedded Technology and Applications Symposium (RTAS’18). IEEE, 13–24.
[26]
Paul Pop, Michael Lander Raagaard, Marina Gutierrez, and Wilfried Steiner. 2018. Enabling fog computing for industrial automation through time-sensitive networking (TSN). IEEE Commun. Stand. Mag. 2, 2 (2018), 55–61.
[27]
Francisco Pozo, Guillermo Rodriguez-Navas, Hans Hansson, and Wilfried Steiner. 2015. SMT-based synthesis of TTEthernet schedules: A performance study. In Proceedings of the 10th IEEE International Symposium on Industrial Embedded Systems (SIES’15). 1–4. DOI:
[28]
Francisco Pozo, Wilfried Steiner, Guillermo Rodriguez-Navas, and Hans Hansson. 2015. A decomposition approach for SMT-based schedule synthesis for time-triggered networks. In Proceedings of the IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA’15). 1–8. DOI:
[29]
Wei Quan, Jinli Yan, Xuyan Jiang, and Zhigang Sun. 2020. On-line traffic scheduling optimization in IEEE 802.1Qch based time-sensitive networks. In Proceedings of the IEEE 22nd International Conference on High Performance Computing and Communications, the IEEE 18th International Conference on Smart City, the IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS’20). 369–376. DOI:
[30]
Michael Lander Raagaard and Paul Pop. 2017. Optimization algorithms for the scheduling of IEEE 802.1 Time-Sensitive Networking (TSN). Technical report, Tech. Univ. Denmark.
[31]
Mauricio G. C. Resende and Celso C. Ribeiro. 2014. GRASP: Greedy Randomized Adaptive Search Procedures. Springer US, Boston, MA, 287–312.
[32]
Eike Schweissguth, Peter Danielis, Dirk Timmermann, Helge Parzyjegla, and Gero Mühl. 2017. ILP-based joint routing and scheduling for time-triggered networks. In Proceedings of the 25th International Conference on Real-time Networks and Systems. 8–17.
[33]
Bassem Sellami, Akram Hakiri, Sadok Ben Yahia, and Pascal Berthou. 2022. Energy-aware task scheduling and offloading using deep reinforcement learning in SDN-enabled IoT network. Comput. Netw. 210 (2022), 108957.
[34]
Giorgio Stampa, Marta Arias, David Sánchez-Charles, Victor Muntés-Mulero, and Albert Cabellos. 2017. A deep-reinforcement learning approach for software-defined networking routing optimization. arXiv preprint arXiv:1709.07080 (2017).
[35]
Ammad Ali Syed, Serkan Ayaz, Tim Leinmüller, and Madhu Chandra. 2021. Dynamic scheduling and routing for TSN based in-vehicle networks. In Proceedings of the IEEE International Conference on Communications Workshops (ICC Workshops’21). 1–6. DOI:
[36]
Yujie Tang, Nan Cheng, Wen Wu, Miao Wang, Yanpeng Dai, and Xuemin Shen. 2019. Delay-minimization routing for heterogeneous VANETs with machine learning based mobility prediction. IEEE Trans. Vehic. Technol. 68, 4 (2019), 3967–3979. DOI:
[37]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2023. Attention Is All You Need. arxiv:1706.03762 [cs.CL]
[38]
Stefano Vitturi, Claudio Zunino, and Thilo Sauter. 2019. Industrial communication systems and their future challenges: Next-generation ethernet, IIoT, and 5G. Proc. IEEE 107, 6 (2019), 944–961. DOI:
[39]
Marek Vlk, Zdeněk Hanzálek, Kateřina Brejchová, Siyu Tang, Sushmit Bhattacharjee, and Songwei Fu. 2020. Enhancing schedulability and throughput of time-triggered traffic in IEEE 802.1Qbv time-sensitive networks. IEEE Trans. Commun. 68, 11 (2020), 7023–7038.
[40]
Chuanyu Xue, Tianyu Zhang, Yuanbin Zhou, Mark Nixon, Andrew Loveless, and Song Han. 2024. Real-Time Scheduling for 802.1Qbv Time-Sensitive Networking (TSN): A Systematic Review and Experimental Study. arxiv:2305.16772 [cs.NI].
[41]
Jinli Yan, Wei Quan, Xuyan Jiang, and Zhigang Sun. 2020. Injection time planning: Making CQF practical in time-sensitive networking. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’20). IEEE, 616–625.
[42]
Chunmeng Zhong, Hongyu Jia, Hai Wan, and Xibin Zhao. 2021. DRLS: A deep reinforcement learning based scheduler for time-triggered ethernet. In Proceedings of the International Conference on Computer Communications and Networks (ICCCN’21). 1–11. DOI:
Index Terms
- Towards Distributed Flow Scheduling in IEEE 802.1Qbv Time-Sensitive Networks
Recommendations
No-wait Packet Scheduling for IEEE Time-sensitive Networks (TSN)
RTNS '16: Proceedings of the 24th International Conference on Real-Time Networks and SystemsThe IEEE Time-sensitive Networking (TSN) Task Group has recently standardized enhancements for IEEE 802.3 networks for enabling it to transport time-triggered traffic (aka scheduled traffic) providing them with stringent bounds on network delay and ...
Scheduling Real-Time Communication in IEEE 802.1Qbv Time Sensitive Networks
RTNS '16: Proceedings of the 24th International Conference on Real-Time Networks and SystemsThe enhancements being developed by the Time-Sensitive Networking Task Group as part of IEEE 802.1 emerge as the future of real-time communication over Ethernet networks for automotive and industrial application domains. In particular IEEE 802.1Qbv is ...
Large-scale periodic scheduling in time-sensitive networks
AbstractThe real-time and safety-critical demands posed in a variety of industrial systems can now be handled on the Ethernet architecture by virtue of the IEEE Time-Sensitive Networking task group. In particular, deterministic nature and ...
Highlights- A novel, efficient, and highly scalable algorithm.
- Tailored for IEEE 802.1Qbv ...
Comments
Information & Contributors
Information
Published In
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Journal Family
Publication History
Published: 23 July 2024
Online AM: 04 July 2024
Accepted: 27 June 2024
Revised: 07 May 2024
Received: 27 December 2023
Published in TOSN Volume 20, Issue 5
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- National Science Foundation of China (NSFC)
- Fundamental Research Funds for the Central Universities
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 374Total Downloads
- Downloads (Last 12 months)374
- Downloads (Last 6 weeks)53
Reflects downloads up to 13 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in