Predictive Scheme for Mixed Transmission in Time-Sensitive Networking

doi:10.12142/ZTECOM.202204010

ZTE Communications ›› 2022, Vol. 20 ›› Issue (4): 78-88.DOI: 10.12142/ZTECOM.202204010

• Research Paper • Previous Articles Next Articles

Predictive Scheme for Mixed Transmission in Time-Sensitive Networking

LI Zonghui¹, YANG Siqi¹, YU Jinghai²(), HE Fei³, SHI Qingjiang^4,⁵

^1.School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
^2.ZTE Corporation, Shenzhen 518057, China
^3.School of Software, Tsinghua University, Beijing 100084, China
^4.School of Software Engineering, Tongji University, Shanghai 201804, China
^5.Shenzhen Research Institute of Big Data, Shenzhen 518172, China

Received:2022-01-04 Online:2022-12-31 Published:2022-12-30
About author:LI Zonghui received his BS degree in computer science from Beijing Information Science and Technology University, China in 2010, and MS and PhD degrees from the Institute of Microelectronics and the School of Software, Tsinghua University, China in 2014 and 2019, respectively. He is currently an associate professor in the School of Computer and Information Technology, Beijing Jiaotong University, China. His research interests include embedded and high performance computing, real-time embedded systems, especially for industrial control networks and time-sensitive networking.|YANG Siqi received her BS degree in network engineering from Hebei University, China. She is currently working toward her master’s degree at Beijing Jiaotong University, China. Her research interest is real-time networks.|YU Jinghai (yu.jinghai@zte.com.cn) received his master’s degree from Nanjing University of Posts and Telecommunications, China in 1999. He is currently working in the Data System Department of ZTE Corporation, with more than 20 years of research and design experience in data network products including BIER, Detnet, TSN, Switch and Router, Data Center and SDN. He has won the 21st China Patent Silver Award and the first prize of the Science and Technology Award of the China Communications Society.|HE Fei is an associate professor at the School of Software of Tsinghua University, China. He received his PhD degree from Tsinghua University in 2008. His research interests include model checking, program verification and automated logic reasoning. He has published over 70 papers in academic journals and international conferences. He is currently on the editor board of Theory of Computing Systems and Frontiers of Computer Science. He has served as the PC member for many formal conferences, including ICSE, ESEC/FSE, CONCUR, FMCAD, SAT, ATVA, APLAS, ICECCS, SETTA, etc.|SHI Qingjiang received his PhD degree in electronic engineering from Shanghai Jiao Tong University, China in 2011. From September 2009 to September 2010, he visited Prof. Z.-Q. (Tom) LUO’s research group at the University of Minnesota, USA. In 2011, he worked as a research scientist at Bell Labs China. From 2012, he was with the School of Information and Science Technology at Zhejiang Sci-Tech University, China. From Feb. 2016 to Mar. 2017, he worked as a research fellow at Iowa State University, USA. From Mar. 2018, he has been a full professor with the School of Software Engineering at Tongji University, China. He is also with the Shenzhen Research Institute of Big Data. His interests lie in algorithm design and analysis with applications in machine learning, signal processing and wireless networks. So far he has published more than 70 IEEE journals and filed about 30 national patents. He has received the Outstanding Technical Achievement Award in 2021, the Huawei Technical Cooperation Achievement Transformation Award (2nd Prize) in 2022, the Golden Medal at the 46th International Exhibition of Inventions of Geneva in 2018, the First Prize of Science and Technology Award from China Institute of Communications in 2017, the National Excellent Doctoral Dissertation Nomination Award in 2013, the Shanghai Excellent Doctoral Dissertation Award in 2012, and the Best Paper Award from the IEEE PIMRC’09 conference.

Abstract

Abstract:

Time-sensitive networking (TSN) is an important research area for updating the infrastructure of industrial Internet of Things. As a product of the integration of the operation technology (OT) and the information technology (IT), it meets the real-time and deterministic nature of industrial control and is compatible with Ethernet to support the mixed transmission of industrial control data and Ethernet data. This paper systematically summarizes and analyzes the shortcomings of the current mixed transmission technologies of the bursty flows and the periodic flows. To conquer these shortages, we propose a predictive mixed-transmission scheme of the bursty flows and the periodic flows. The core idea is to use the predictability of time-triggered transmission of TSN to further reduce bandwidth loss of the previous mixed-transmission methods. This paper formalizes the probabilistic model of the predictive mixed transmission mechanism and proves that the proposed mechanism can effectively reduce the loss of bandwidth. Finally, based on the formalized probabilistic model, we simulate the bandwidth loss of the proposed mechanism. The results demonstrate that compared with the previous mixed-transmission method, the bandwidth loss of the proposed mechanism achieves a 79.48% reduction on average.

Key words: time-sensitive networking, 802.1Qbv, 802.1Qbu, guard band strategy, preemption strategy

LI Zonghui, YANG Siqi, YU Jinghai, HE Fei, SHI Qingjiang. Predictive Scheme for Mixed Transmission in Time-Sensitive Networking[J]. ZTE Communications, 2022, 20(4): 78-88.

Figures/Tables 13

Figure 1 Time-aware shaper (TAS) defined by IEEE 802.1Qbv

Figure 2 Guard band strategy defined by 802.1Qbv

Figure 3 Preemption strategy defined by 802.1Qbu

Figure 4 Mixed strategy of guard band and preemption

Figure 5 Remaining time transmission strategy and optimal preemption strategy

Table 1 Expected bandwidth loss: the remaining time transmission strategy (RemainedTimeLoss) vs the guard band strategy (Guardloss) of 802.1QBV

X Distribution Within the Range of Length [64, 1 518]	RemainedTimeLoss/byte	Guardloss/byte
Uniform distribution	395.5	1 122.5
Binomial distribution	363.75	1 154.2
Poisson distribution	395.5	1 122.5
Normal distribution	395.75	1 122.3

Figure 6 Comparison of the expected bandwidth loss between the remaining time transmission strategy and the guard band strategy of 802.1Qbv

Table 2 Expected bandwidth loss: the optimal preemption strategy (OPLoss) vs the mixed strategy (MixedLoss) of the guard band and the frame preemption

X Distribution Within the Range of Length [64, 1 518]	OPLoss/byte	MixedLoss/byte	Reduced Rate/byte
Uniform distribution	29.08	131.43	77.87%
Binomial distribution	27.31	135.96	79.91%
Poisson distribution	27.05	136.52	80.19%
Normal distribution	27.28	136.01	79.94%

Figure 7 Comparison of the expected bandwidth loss between the optimal preemption strategy (OPLoss) and the mixed strategy (MixedLoss) of the guard band and the frame preemption

Table 3 Expected bandwidth loss of each part: the optimal preemption strategy (OPLoss) vs the mixed strategy (MixedLoss) in Uniform distribution

Uniform Distribution Within the Range of Length [64, 1 518]	OPLoss/byte	MixedLoss/byte
X < 124	1.93	3.14
X ≥ 124, Y < 60	3.04	11.61
X ≥ 124, Y ≥ 60, X - Y ≥ 64	17.89	106.61
X ≥ 124, Y ≥ 60, X - Y < 64	6.22	10.07

Table 4 Expected bandwidth loss of each part: the optimal preemption strategy (OPLoss) vs the mixed strategy (MixedLoss ) in Binomial distribution

Binomial Distribution (p=0.5) Within the Range of Length [64, 1 518]	OPLoss/byte	MixedLoss/byte
X < 124	0	0
X ≥ 124, Y < 60	2.43	9.27
X ≥ 124, Y ≥ 60, X - Y ≥ 64	19.91	118.64
X ≥ 124, Y ≥ 60, X - Y < 64	4.97	8.04

Table 5 Expected bandwidth loss of each part: the optimal preemption strategy (OPLoss) vs the mixed strategy (MixedLoss) in poisson distribution

Poisson Distribution Within the Range of Length [64, 1 518]	OPLoss/byte	MixedLoss/byte
X < 124	0	0
X ≥ 124, Y < 60	2.24	8.53
X ≥ 124, Y ≥ 60, X - Y ≥ 64	20.24	120.58
X ≥ 124, Y ≥ 60, X - Y < 64	4.57	7.4

Table 6 Expected bandwidth loss of each part: the optimal preemption strategy (OPLoss) vs the mixed strategy (MixedLoss) in normal distribution

Normal distribution within the range of length [64, 1 518]	OPLoss/byte	MixedLoss/byte
X < 124	0.016	0.028 9
X ≥ 124, Y < 60	2.41	9.18
X ≥ 124, Y ≥ 60, X - Y ≥ 64	19.94	118.83
X ≥ 124, Y ≥ 60, X - Y < 64	4.92	7.96

References 16

1	HERMANN M, PENTEK T, OTTO B. Design principles for industrie 4.0 scenarios [C]//The 49th Hawaii International Conference on System Sciences. IEEE, 2016: 3928–3937
2	ISO. Road vehicles: controller area network (CAN) (part 2): high-speed medium access unit. [S]. 2016
3	International Electrotechnical Commission. Electric railway equipment: Train bus (Part 2): train communication network conformance testing. [S]. 2007
4	DECOTIGNIE J D. Ethernet-based real-time and industrial communications [J]. Proceedings of the IEEE, 2005, 93(6): 1102–1117. DOI: 10.1109/JPROC.2005.849721 DOI
5	BELLO L LO, STEINER W. A perspective on IEEE time-sensitive networking for industrial communication and automation systems [J]. Proceedings of the IEEE, 2019, 107(6): 1094–1120. DOI: 10.1109/JPROC.2019.2905334 DOI
6	International Group SAE. Time-triggered Ethernet. Aerospace standard: AS6802 [S]. 2011
7	International Group SAE. TTP Communication Protocol. Aerospace standard: AS6003 [S]. 2011
8	STEINER W. An evaluation of SMT-based schedule synthesis for time-triggered multi-hop networks [C]//The 31st IEEE Real-Time Systems Symposium. IEEE, 2010: 375–384. DOI: 10.1109/RTSS.2010.25 DOI
9	IEEE. Standard for a precision clock synchronization protocol for networked measurement and control systems. IEEE std 1588-2002 [S]. 2002. DOI: 10.1109/IEEES
10	IEEE. IEEE standard for local and metropolitan area networks-timing and synchronization for time-sensitive applications in bridged local area networks. IEEE std 802 1AS-2011 [S]. 2011
11	LI Z H, WAN H, DENG Y D, et al. A flattened-priority framework for mixed-criticality systems [J]. IEEE transactions on industrial electronics, 2020, 67(11): 9862–9872. DOI: 10.1109/TIE.2019.2956406 DOI
12	LI Z H, WAN H, DENG Y D, et al. A resource-efficient priority scheduler for time-sensitive networking switches [J]. CCF transactions on networking, 2020, 3(1): 21–34. DOI: 10.1007/s42045-020-00034-x DOI
13	IEEE. IEEE standard for local and metropolitan area networks-audio video bridging (AVB) systems [S]. 2011
14	IEEE. IEEE standard for local and metropolitan area networks-virtual bridged local area networks amendment 12: Forwarding and queuing enhancements for time-sensitive streams. IEEE std 802 1Qav-2009 [S]. 2010
15	IEEE. IEEE standard for local and metropolitan area networks-bridges and bridged networks-amendment 25: Enhancements for scheduled traffic. IEEE Std 802.1 [S]. 2011
16	IEEE. IEEE standard for local and metropolitan area networks: bridges and bridged networks: amendment 26: frame preemption. IEEE std 802 1Qbu-2016 [S]. 2016

Predictive Scheme for Mixed Transmission in Time-Sensitive Networking

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