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改进以太网 AVB 网络中附加流预留类业务的最坏情况延迟分析。

Improving Worst-Case Delay Analysis for Traffic of Additional Stream Reservation Class in Ethernet-AVB Network.

机构信息

School of Electronic and Information Engineering, Beihang University, Beijing 100191, China.

出版信息

Sensors (Basel). 2018 Nov 9;18(11):3849. doi: 10.3390/s18113849.

Abstract

With the increase in the number of Electronic Control Units (ECUs) and future requirements for vehicle functions, two SR (Stream Reservation) traffic classes may not be sufficient to ensure fulfilment of constraints for multiple traffic types with individual timing requirements transmitted in the Ethernet-AVB (Audio Video Bridging) networks. The goal of this paper is to determine the worst-case delay for an additional SR traffic class under the CBS (Credit-Based Shaper) algorithm. Delay evaluation is based on the impact analysis of CBS on different priority flows, particularly depending on when the credits of both SR class A and B drain from the worst-case perspective. More specifically, both the impact of CBS and the evolution trends of credit on different priority class flows are first analyzed from the worst-case perspective. Then, for an additional SR class, two types of worst-case delay models are established with the CBS configuration suggestions. Finally, an approach to calculate the worst-case queuing delay is proposed. Moreover, the worst-case end-to-end delay is determined by the network calculus approach and simulation. Numerical results show that the delay bounds of our models are tighter than those of other models, which is beneficial to the development of Ethernet-AVB for in-vehicle networking.

摘要

随着电子控制单元 (ECU) 的数量增加以及未来对车辆功能的要求,两个 SR(流预留)流量类别可能不足以确保在以太网-AVB(音视频桥接)网络中传输的具有单独定时要求的多种流量类型的约束得到满足。本文的目的是确定在 CBS(基于信用的整形器)算法下额外的 SR 流量类别最坏情况下的延迟。延迟评估基于 CBS 对不同优先级流的影响分析,特别是取决于最坏情况下 A 类和 B 类 SR 流的信用何时耗尽。更具体地说,首先从最坏情况的角度分析了 CBS 的影响以及不同优先级类流上信用的演变趋势。然后,针对额外的 SR 类,根据 CBS 配置建议建立了两种最坏情况延迟模型。最后,提出了一种计算最坏情况排队延迟的方法。此外,最坏情况下的端到端延迟通过网络演算方法和仿真来确定。数值结果表明,我们的模型的延迟边界比其他模型更紧,这有利于车载网络中以太网-AVB 的发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e62/6263623/a6d4aeb35ccb/sensors-18-03849-g0A1.jpg

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