王波, 高文炜, 徐丹妮, 贺新乐. 一种基于NeSTiNg的TSN强实时流量调度和自动配置方法[J]. 微电子学与计算机, 2022, 39(11): 62-68. DOI: 10.19304/J.ISSN1000-7180.2022.0218
引用本文: 王波, 高文炜, 徐丹妮, 贺新乐. 一种基于NeSTiNg的TSN强实时流量调度和自动配置方法[J]. 微电子学与计算机, 2022, 39(11): 62-68. DOI: 10.19304/J.ISSN1000-7180.2022.0218
WANG Bo, GAO Wenwei, XU Danni, HE Xinle. A strong real-time traffic scheduling and automatic configuration method for TSN based on NeSTiNg[J]. Microelectronics & Computer, 2022, 39(11): 62-68. DOI: 10.19304/J.ISSN1000-7180.2022.0218
Citation: WANG Bo, GAO Wenwei, XU Danni, HE Xinle. A strong real-time traffic scheduling and automatic configuration method for TSN based on NeSTiNg[J]. Microelectronics & Computer, 2022, 39(11): 62-68. 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

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

  • 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.

     

    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.

     

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