A Post-failure Traffic Reconfiguration and Scheduling Optimization Method for Time-sensitive Networking

doi:10.12382/bgxb.2024.0141

Abstract

Abstract:

In the existing studies on time-triggered (TT) traffic reconfiguration in time-sensitive networking (TSN), the redundancy status of the traffic is often overlooked, making it difficult to balance both TT traffic latency performance and the efficiency of reconfiguration scheme solutions in practical deployments. To address this issue, an optimization method for traffic reconfiguration and scheduling after failures is proposed. This method improves the solution efficiency of the reconfiguration scheme by introducing a fast reconfiguration algorithm and its corresponding enhancements at the expense of the latency performance of non-redundant traffic while ensuring solution success. Additionally, the proposed method flexibly adjusts the scheduling strategy of unaffected TT traffic, and incorporates a tabu search-based optimization algorithm with an associated objective function to enhance the overall TT traffic latency performance after reconfiguration. Experimental results demonstrate that the proposed method improves the solution efficiency by more than 75.03% compared to the commonly used incremental reconfiguration methods, and slightly improves TT traffic latency performance after multiple rounds of reconfiguration, showing a significant potential for practical application.

Key words: time-sensitive networking, time-triggered traffic, traffic planning, reconfiguration, optimization method

CLC Number:

TP393.1

LI Ji, GUO Yonghong, NIU Haitao, GUO Xin, HOU Zeng. A Post-failure Traffic Reconfiguration and Scheduling Optimization Method for Time-sensitive Networking[J]. Acta Armamentarii, 2024, 45(11): 3970-3982.

Figures/Tables 11

Fig.1 Control plane model structure design diagram

Fig.2 Flowchart of the implementation of joint optimization method for flow planning and reconfiguration

Fig.3 Flowchart of fast reconfiguration phase realization

Fig.4 Example of the localized route search algorithm

Fig.5 Example of the schedulable interval

Fig.6 Flowchart of optimization planning phase

Fig.7 Tabu search-based optimization

Fig.8 Topology diagram of a vehicle integrated electronic system containing four domain controllers

Table 1 Traffic parameters

流量	周期/ ms	传输时隙/μs	初始冗余等级	单流量副本带宽/%
关键控制	10	100	2	≈1
雷达	20	100	2	≈3
激光雷达	20	1200	2	≈18
影像	50	400	2	≈18
远程信息处理	50	1000	2	≈2
其他控制	50	500	2	≈1

Fig.9 Simulation analysis of typical vehicle integrated electronic system networks

Fig.10 Simulation analysis of typical spacecraft integrated electronic system networks

References 24

[1]	ZHANG F, YAO G W, WANG Q, et al. An open integrated electronic system software architecture design for launch vehicle[C]//Proceedings of Communications, Signal Processing, and Systems. Dalian, China:Springer, 2020.
[2]	段建民, 石慧, 战宇辰. 基于机器视觉筛选GPS卫星信号的无人驾驶汽车组合导航方法[J]. 电子技术应用, 2016, 42(1):111-114.
	DUAN J M, SHI H, ZHAN Y C. Integrated navigation system for unmanned intelligent vehicle based on vision[J]. Application of Electronic Technique, 2016, 42(1):111-114. (in Chinese)
[3]	赵继东, 盖振伟, 李晶, 等. 车辆通信设备集成化体系结构[J]. 兵工学报, 2022, 43(增刊1): 21-25.
	ZHAO J D, GAI Z W, LI J, et al. Integrated structure of communication equipment for military vehicle[J]. Acta Armamentarii, 2022, 43(S1): 21-25. (in Chinese) doi: 10.12382/bgxb.2022.A002
[4]	HU X H, CHEN X H, GUO J P, et al. Optimization model for bus priority control considering carbon emissions under non-bus lane conditions[J]. Journal of Cleaner Production, 2023, 402: 136747.
[5]	LIU Y H, ZUO X Q, AI G Q, et al. A construction-and-repair based method for vehicle scheduling of bus line with branch lines[J]. Computers & Industrial Engineering, 2023, 178: 109103.
[6]	MA D F, FANG B, MA W H, et al. Potential routes extraction for urban customized bus based on vehicle trajectory clustering[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(11):11878-11888.
[7]	JIANG Q, HU C, ZHAO B X, et al. Scalable 3D object detection pipeline with center-based sequential feature aggregation for intelligent vehicles[J]. IEEE Transactions on Intelligent Vehicles, 2023, 9(1):1512-1523.
[8]	LOO B P Y, FAN Z Y, LIAN T, et al. Using computer vision and machine learning to identify bus safety risk factors[J]. Accident Analysis & Prevention, 2023, 185: 107017.
[9]	BETHGE D, COELHO L F, KOSCH T, et al. Technical design space analysis for unobtrusive driver emotion assessment using multi-domain context[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2023, 6(4): 1-30.
[10]	PENG Y F, SHI B X, JIANG T G, et al. A survey on in-vehicle time sensitive networking[J]. IEEE Internet of Things Journal, 2023, 10(16):14375-14396.
[11]	XU Y L, SHANG J, TANG H. Recent trends of in-vehicle time sensitive networking technologies, applications and challenges[J]. China Communications, 2023, 20(11):30-55. doi: 10.23919/JCC.ea.2021-0888.202302
[12]	TRIFONOV H, HEFFEMAN D. OPC UA TSN: a next-generation network for Industry 4.0 and IIoT[J]. International Journal of Pervasive Computing and Communications, 2023, 19(3): 386-411.
[13]	NAYAK N G, DURR F, ROTHERMEL K. Incremental flow scheduling and routing in time-sensitive software-defined networks[J]. IEEE Transactions on Industrial Informatics, 2017, 14(5): 2066-2075.
[14]	ARESTOVA A, BARON W, HIELSCHER K S J, et al. ITANS: incremental task and network scheduling for time-sensitive networks[J]. IEEE Open Journal of Intelligent Transportation Systems, 2022, 3: 369-387.
[15]	GARTNER C, RIZK A, KOLDEHOFE B, et al. On the incremental reconfiguration of time-sensitive networks at runtime[C]// Proceedings of 2022 IFIP Networking Conference. Catania, Italy: IEEE, 2022: 1-9.
[16]	XU L, XU Q M, CHEN C L, et al. Efficient task-network scheduling with task conflict metric in time-sensitive networking[J]. IEEE Transactions on Industrial Informatics, 2023, 20(2):1528-1538.
[17]	FENG Z W, GU Z H, YU H C, et al. Online rerouting and rescheduling of time-triggered flows for fault tolerance in time-sensitive networking[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2022, 41(11): 4253-4264.
[18]	LI J, XIONG H G, LI Q, et al. Run-time reconfiguration strategy and implementation of time-triggered networks[J]. Electronics, 2022, 11(9): 1477.
[19]	FALK J, GEPPERT H, DURR F, et al. Dynamic QoS-aware traffic planning for time-triggered flows in the real-time data plane[J]. IEEE Transactions on Network and Service Management, 2022, 19(2): 1807-1825.
[20]	POZO F, RODRIGUEZ-NAVAS G, HANSSON H. Schedule reparability: enhancing time-triggered network recovery upon link failures[C]// Proceedings of the 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications. Hakodate, Japan: IEEE, 2018: 147-156.
[21]	HCKEL T, MEYER P, KORF F, et al. Secure time-sensitive software-defined networking in vehicles[J]. IEEE Transactions on Vehicular Technology, 2023, 72(1):35-51.
[22]	SUN W J, ZOU Y, ZHANG X D, et al. Joint routing and scheduling optimization of in vehicle time sensitive networks based on improved grey wolf optimizer[J]. IEEE Internet of Things Journal, 2023, 11(4):7093-7106.
[23]	LI J, LI Q, XIONG H. Enhancing low-priority traffic reconfiguration designs in mixed-critical avionics networks[J]. IET Communications, 2023, 17(13):1524-1540.
[24]	LI Y T, JIANG J H, HONG S H. Joint traffic routing and scheduling algorithm eliminating the nondeterministic interruption for tsn networks used in IIoT[J]. IEEE Internet of Things Journal, 2022, 9(19): 18663-18680.