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Probabilistic upper bounds for heterogeneous flows using a Static Priority Queueing on an AFDX network

机译:使用AFDX网络上使用静态优先级排队的异构流的概率上限

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AFDX (Avionics Full Duplex Switched Ethernet, ARINC 664) developed for the Airbus A380 represents a major upgrade in both bandwidth and capability. Its reliance on Ethernet technology helps to lower some implementation costs, but guaranteed service presents challenges for system designers. An analysis of end-to-end transfer delays through the network is required in order to determine upper bounds. The stochastic network calculus approach analytically determines worst-case probabilistic upper bounds in the context of homogeneous avionics flows without priorities. Such upper bounds can be exceeded with a given probability P_(UB), and are relevant in the context of avionics, where functions are designed to give accurate results even if they miss some frames. Nowadays, there is a need to handle new classes of traffics with different priorities (voice, video, best-effort, ...) on the same AFDX network with no consequences on existing avionic flows. This paper presents the application of the stochastic network calculus approach in the context of a static priority queueing service discipline and evaluates the influence of the service discipline on analytical probabilistic upper bounds.
机译:为空中客车A380开发的AFDX(AviONICS全双工交换以太网,ARINC 664)代表了带宽和能力的重大升级。它依赖于以太网技术有助于降低一些实施成本,但保证服务为系统设计师提供挑战。需要通过网络进行端到端传输延迟,以确定上限。随机网络微积分方法在没有优先事项的情况下,在均匀的航空电子流量的背景下分析了最坏情况的概率上限。在给定的概率P_(UB)中可以超过这种上限,并且在航空电子设备的背景下是相关的,其中函数旨在为其提供准确的结果,即使它们错过了一些帧。如今,需要在同一AFDX网络上处理具有不同优先级(语音,视频,最佳努力,...)的新类流量,而没有对现有航空数据流的后果。本文提出了随机网络微积分方法在静态优先级排队服务纪律的背景下的应用,并评估了服务学科对分析概率上限的影响。

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