Data flow control for network load balancing in IEEE time sensitive networks for automation
Data flow control for network load balancing in IEEE time sensitive networks for automation
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.
Automation, Automation networks, Delays, Load management, Prediction algorithms, Routing, Synchronization, Throughput, Time Sensitive Networks, data flow control, load balancing, time sensitive networks
14044-14060
Weichlein, Thomas
52971363-caf9-4edf-bdba-b90f8ca53459
Zhang, Shujun
e918def5-c5a1-46a9-9946-6a5cd6ee986c
Li, Pengzhi
cef632f5-cbdf-40ce-a20f-00d22cc007dc
Zhang, Xu
21e210aa-51db-40af-a91b-f64bf44ed143
8 February 2023
Weichlein, Thomas
52971363-caf9-4edf-bdba-b90f8ca53459
Zhang, Shujun
e918def5-c5a1-46a9-9946-6a5cd6ee986c
Li, Pengzhi
cef632f5-cbdf-40ce-a20f-00d22cc007dc
Zhang, Xu
21e210aa-51db-40af-a91b-f64bf44ed143
Weichlein, Thomas, Zhang, Shujun, Li, Pengzhi and Zhang, Xu
(2023)
Data flow control for network load balancing in IEEE time sensitive networks for automation.
IEEE Access, 11, .
(doi:10.1109/ACCESS.2023.3243286).
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.
Text
Data_Flow_Control_for_Network_Load_Balancing_in_IEEE_Time_Sensitive_Networks_for_Automation
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More information
Accepted/In Press date: 24 January 2023
e-pub ahead of print date: 8 February 2023
Published date: 8 February 2023
Additional Information:
Publisher Copyright:
© 2013 IEEE.
Keywords:
Automation, Automation networks, Delays, Load management, Prediction algorithms, Routing, Synchronization, Throughput, Time Sensitive Networks, data flow control, load balancing, time sensitive networks
Identifiers
Local EPrints ID: 475076
URI: http://eprints.soton.ac.uk/id/eprint/475076
ISSN: 2169-3536
PURE UUID: 3baf5ac5-a77a-45a9-b3cd-713ef413b13e
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Date deposited: 09 Mar 2023 19:02
Last modified: 05 Jun 2024 19:09
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Author:
Thomas Weichlein
Author:
Shujun Zhang
Author:
Pengzhi Li
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