Data Flow Control for Network Load Balancing in IEEE Time Sensitive Networks for Automation

Weichlein, Thomas, Zhang, Shujun ORCID: 0000-0001-5699-2676, Li, Pengzhi ORCID: 0000-0001-8883-1885 and Zhang, Xu (2023) Data Flow Control for Network Load Balancing in IEEE Time Sensitive Networks for Automation. IEEE Access, 11. pp. 14044-14060. doi:10.1109/access.2023.3243286

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Abstract

IEEE time sensitive networks (TSN) offer redundant paths for automation networks that are essential preconditions for network load balancing (NLB) or distribution. They also provide several traffic shapers and schedulers with different impacts on the data flow control. The selection of the right traffic shaper or scheduler for an automation network is challenging. Their influence depends on various network parameters such as network extension, network cycles, application cycles, and the amount of data per traffic class and network cycle. In this study, data flow control for NLB in automation TSN using different traffic shapers and schedulers was investigated. The effects of the network parameters on the shapers and schedulers were derived and imported into the data flow control model of the automation network. The sample networks were simulated, and performance comparisons were made. The results show that the enhancements for scheduled traffic (EST), strict priority queuing (SPQ), and the combination of SPQ with frame preemption (FP) are better scheduler selections in connection with larger networks, fast network cycles, and fast application cycles. The cyclic queuing and forwarding (CQF) shaper and asynchronous traffic shaper (ATS) are rather an alternative for load control in small networks or in conjunction with slow applications.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Automation networks; data flow control; load balancing; time sensitive networks
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Anne Pengelly
Date Deposited:	08 Mar 2023 14:16
Last Modified:	31 Oct 2023 11:22
URI:	https://eprints.glos.ac.uk/id/eprint/12449

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