一种基于NeSTiNg的TSN强实时流量调度和自动配置方法

王波; 高文炜; 徐丹妮; 贺新乐

doi:10.19304/J.ISSN1000-7180.2022.0218

一种基于NeSTiNg的TSN强实时流量调度和自动配置方法

doi: 10.19304/J.ISSN1000-7180.2022.0218

西安微电子技术研究所，陕西西安 710054

详细信息

作者简介:
王波男，(1993-)，硕士研究生.研究方向为嵌入式计算机应用技术.E-mail: wangbo735856686@163.com

高文炜男，(1975-)，硕士，研究员，硕士生导师.研究方向为嵌入式系统

徐丹妮女，(1989-)，硕士，高级工程师.研究方向为实时以太网通信技术

贺新乐男，(1997-)，硕士研究生.研究方向为嵌入式计算机应用技术

中图分类号: TP393
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出版历程
- 收稿日期: 2022-04-01
- 修回日期: 2022-05-10
- 网络出版日期: 2022-11-29

A strong real-time traffic scheduling and automatic configuration method for TSN based on NeSTiNg

Xi′an Microelectronics Technology Institute, Xi′an 710054, China

摘要

摘要:
流量调度是时间敏感网络(Time Sensitive Networking，TSN)技术的核心.在大型工业嵌入式系统应用中，TSN流量调度的设计和仿真阶段需要对链路、流、门控等调度信息进行复杂且苛刻的配置.现有的基于仿真框架的配置方法多采用手动配置，这阻碍了这些框架对大型TSN网络的适用性.基于TSN强实时流量传输机制和NeSTiNg(Network Simulator for Time-Sensitive Networking)框架，提出一种适用于大型网络的强实时流量调度和自动配置方法.首先，该方法区别于传统的手动配置输入方式，实现了对流偏移、门状态和路由表等信息的自动配置; 其次，该方法提取了流的端到端时延，验证了大型网络中TSN传输的强实时性; 最后，通过计算拓扑变化后选择新路径的运算时间，评估了该方法对动态拓扑的适用性.结果表明：该方法保证了大型网络中大量流传输的强实时性，且当拓扑改变时，相比于静态路由有更低的运算时间.
- TSN /
- IEEE 802.1Qbv /
- NeSTiNg /
- 流量调度 /
- 自动配置 /
- TSN评估 /
- 运算时间
Abstract:
Traffic scheduling is the core of Time Sensitive Networking (TSN) technology. In large-scale industrial embedded system applications, the design and simulation stages of TSN traffic scheduling require complex and demanding configuration of scheduling information such as links, flows, and gating. Existing configuration methods based on simulation frameworks mostly use manual configuration, which hinders the applicability of these frameworks to large-scale TSN networks. Based on the strong real-time traffic transmission mechanism of TSN and the NeSTiNg (Network Simulator for Time-Sensitive Networking) framework, this paper proposes a strong real-time traffic scheduling and automatic configuration method suitable for large networks. First, this method is different from the traditional manual configuration input method, and realizes the automatic configuration of information such as flow offsets, gate status and routing tables; secondly, the method extracts the end-to-end delay of the flow, which verifies the strong real-time performance of TSN transmission in large networks; finally, the applicability of the method to dynamic topology is evaluated by calculating the computing time of selecting a new path after topology changes. The results show that this method guarantees strong real-time transmission of large numbers of flows in a large network, and when the topology changes, it has a lower computing time than static routing.
- TSN /
- IEEE 802.1Qbv /
- NeSTiNg /
- traffic scheduling /
- automatic configuration /
- evaluation of TSN /
- computing time

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图 1 IEEE802.1 Qbv强实时流量传输机制

Figure 1. Strong real-time traffic transmission mechanism of IEEE 802.1Qbv

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图 2 自动配置方法

Figure 2. Automatic configuration method

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图 3 自动配置输入方法流程图

Figure 3. Automatic configuration input method flowchart

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图 4 结果提取

Figure 4. Result extraction

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图 5 网络拓扑图

Figure 5. Network topology

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表 1 40组基本流的最大端到端延迟

Table 1. Maximum end to end delay of 40 basic flows

ID	TX	RX	周期/μs	延迟/μs	ID	TX	RX	周期/μs	延迟/μs
1	ES0	ES6	5 000	51	21	ES6	ES7	5 000	32
2	ES1	ES7	2 000	50	22	ES6	ES8	2 000	24
3	ES1	ES8	1 000	52	23	ES7	ES1	5 000	58
4	ES1	ES9	2 000	92	24	ES7	ES2	5 000	77
5	ES1	ES10	5 000	37	25	ES7	ES4	1 000	71
6	ES2	ES3	1 000	94	26	ES8	ES0	2 000	158
7	ES2	ES7	1 000	82	27	ES8	ES10	1 000	71
8	ES3	ES1	5 000	81	28	ES8	ES11	5 000	74
9	ES3	ES2	5 000	66	29	ES9	ES0	5 000	971
10	ES3	ES4	2 000	25	30	ES9	ES1	5 000	52
11	ES3	ES5	2 000	38	31	ES9	ES3	5 000	38
12	ES4	ES3	5 000	52	32	ES10	ES2	5 000	62
13	ES4	ES8	1 000	67	33	ES10	ES5	5 000	71
14	ES4	ES9	1 000	97	34	ES10	ES6	1 000	118
15	ES4	ES11	5 000	52	35	ES10	ES7	5 000	135
16	ES5	ES0	2 000	779	36	ES10	ES11	5 000	39
17	ES5	ES2	5 000	86	37	ES11	ES2	5 000	61
18	ES5	ES4	2 000	38	38	ES11	ES7	1 000	97
19	ES5	ES6	5 000	53	39	ES11	ES9	5 000	43
20	ES6	ES5	5 000	61	40	ES11	ES10	5 000	48

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表 2 增加流的运算时间对比

Table 2. Computing time comparison when adding streams

流数目	运算时间/s
流数目	静态调度	自动配置
10+1	0.192	0.021
20+1	0.482	0.056
30+1	1.13	0.112

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表 3 交换机故障的运算时间对比

Table 3. Computing time comparison when a switch fails

流数目	运算时间/s
流数目	静态调度	自动配置
10	0.023 8	0.013
20	0.318 2	0.188
30	0.749 9	0.334

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参考文献(12)

[1]	JIANG J H, LI Y T, HONG S H, et al. A time-sensitive networking (TSN) simulation model based on OMNET++[C]//2018 IEEE International Conference on Mechatronics and Automation (ICMA). Changchun: IEEE, 2018: 643-648. DOI: 10.1109/ICMA.2018.8484302.
[2]	冯泽坤, 龚龙庆, 徐丹妮, 等. 时间敏感网络中基于ILP的动态流量均衡调度算法[J]. 微电子学与计算机, 2021, 38(6): 33-37. DOI: 10.19304/j.cnki.issn1000-7180.2021.06.006. FENG Z K, GONG L Q, XU D N, et al. ILP-based dynamic flow balancing scheduling algorithm in time sensitive network[J]. Microelectronics & Computer, 2021, 38(6): 33-37. DOI: 10.19304/j.cnki.issn1000-7180.2021.06.006.
[3]	IEEE. IEEE 802.1 Time-sensitive networking (TSN) task group[S]. IEEE.
[4]	CRACIUNAS S S, OLIVER R S, AG T. An overview of scheduling mechanisms for time sensitive networks[C]//Proceedings of the Real-Time Summer School LÉcole dÉté Temps Réel (ETR). 2017: 1551-3203.
[5]	HOUTAN B, BERGSTRÖM A, ASHJAEI M, et al. An automated configuration framework for TSN networks[C]//2021 22nd IEEE International Conference on Industrial Technology (ICIT). Valencia: IEEE, 2021: 771-778. DOI: 10.1109/ICIT46573.2021.9453628.
[6]	HELLMANNS D, FALK J. Nesting - network simulator for time sensitive networking[EB/OL]. [2022-03-15]. https://gitlab.com/ipvs/nesting.
[7]	IEEE. IEEE Std 802.1Qbv-2015 IEEE standard for local and metropolitan area networks-bridges and bridged networks-amendment 25: enhancements for scheduled traffic[S]. Piscataway, NJ: IEEE, 2016. DOI: 10.1109/IEEESTD.2016.8613095.
[8]	张磊, 王盼盼. 时间敏感网络流量整形技术综述[J]. 微电子学与计算机, 2022, 39(1): 46-53. DOI: 10.19304/J.ISSN1000-7180.2021.0629. ZHANG L, WANG P P. Survey of traffic shaping and scheduling in time-sensitive network[J]. Microelectronics & Computer, 2022, 39(1): 46-53 DOI: 10.19304/J.ISSN1000-7180.2021.0629.
[9]	FALK J, HELLMANNS D, CARABELLI B, et al. NeSTiNg: simulating IEEE time-sensitive networking (TSN) in OMNeT++[C]//2019 International Conference on Networked Systems (NetSys). Munich: IEEE, 2019: 1-8. DOI: 10.1109/NetSys.2019.8854500.
[10]	OMNeT++ discrete event simulator[EB/OL]. (2022-03-15). https://omnetpp.org/.
[11]	INET framework[EB/OL]. [2022-03-14]. https://inet.omnetpp.org.
[12]	An open-source S/OMT solver: z3-solver 4.9.1.0[EB/OL]. (2022-03-07). https://pypi.org/project/z3-solver/.