ML-Assisted Latency Assignments in Time-Sensitive Networking

Recent developments in industrial automation and in-vehicle communication have raised the requirements of real-time networking. Bus systems that were traditionally deployed in these fields cannot provide sufficient bandwidth and are now shifting towards Ethernet for their real-time communication needs. In this field, standardization efforts from the IEEE and the IETF have developed new data plane mechanisms such as shapers and schedulers, as well as control plane mechanisms such as reservation protocols to support their new requirements. However, their implementation and their optimal configuration remain an important factor for their efficiency. This work presents a machine learning framework that takes on the configuration task. Four different models are trained for the configuration of per-hop latency guarantees in a distributed resource reservation process and compared with respect to their real-time traffic capacity. The evaluation shows that all models provide good configurations for the provided scenarios, but more importantly, they represent a first step for a semi-automated configuration of parameters in Time-Sensitive Networking.

[1]  Christopher Leckie,et al.  Applying Reinforcement Learning to Packet Scheduling in Routers , 2003, IAAI.

[2]  John Wroclawski,et al.  The Use of RSVP with IETF Integrated Services , 1997, RFC.

[3]  Junaid Qadir,et al.  Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges , 2017, IEEE Access.

[4]  IEEE Standard for Local and Metropolitan Area Networks--Audio Video Bridging (AVB) Systems , 2022 .

[5]  Brighten Godfrey,et al.  A Deep Reinforcement Learning Perspective on Internet Congestion Control , 2019, ICML.

[6]  Alex Graves,et al.  Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.

[7]  Jun Terada,et al.  Low-latency routing for fronthaul network: A Monte Carlo machine learning approach , 2017, 2017 IEEE International Conference on Communications (ICC).

[8]  Sergey Levine,et al.  Continuous Deep Q-Learning with Model-based Acceleration , 2016, ICML.

[9]  Sasikumar Punnekkat,et al.  Self-configuration of IEEE 802.1 TSN networks , 2017, 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA).

[10]  Ian F. Akyildiz,et al.  QoS-Aware Adaptive Routing in Multi-layer Hierarchical Software Defined Networks: A Reinforcement Learning Approach , 2016, 2016 IEEE International Conference on Services Computing (SCC).

[11]  Raouf Boutaba,et al.  A comprehensive survey on machine learning for networking: evolution, applications and research opportunities , 2018, Journal of Internet Services and Applications.

[12]  Tobias Hoßfeld,et al.  Bounded Latency with Bridge-Local Stream Reservation and Strict Priority Queuing , 2020, 2020 11th International Conference on Network of the Future (NoF).

[13]  Ying-Chang Liang,et al.  Applications of Deep Reinforcement Learning in Communications and Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.

[14]  Flavio Esposito,et al.  Scalable Provisioning of Virtual Network Functions via Supervised Learning , 2019, 2019 IEEE Conference on Network Softwarization (NetSoft).

[15]  Nitin Desai,et al.  Enhancing Fault Detection in Time Sensitive Networks using Machine Learning , 2020, 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS).

[16]  Wolfgang Kellerer,et al.  Algorithm-data driven optimization of adaptive communication networks , 2017, 2017 IEEE 25th International Conference on Network Protocols (ICNP).

[17]  Nei Kato,et al.  Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning , 2017, IEEE Transactions on Computers.

[18]  Li Gang,et al.  Stream-based Machine Learning for Real-time QoE Analysis of Encrypted Video Streaming Traffic , 2019, 2019 22nd Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN).

[19]  Albert Cabellos-Aparicio,et al.  A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization , 2017, ArXiv.

[20]  Nicolas Navet,et al.  On the use of supervised machine learning for assessing schedulability: application to ethernet TSN , 2019, RTNS.

[21]  Soheil Samii,et al.  Urgency-Based Scheduler for Time-Sensitive Switched Ethernet Networks , 2016, 2016 28th Euromicro Conference on Real-Time Systems (ECRTS).

[22]  Michael L. Littman,et al.  Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach , 1993, NIPS.