Abstract

In the high-tech and automotive industry, bandwidth considerations and widely accepted standardization are two important reasons why Ethernet is currently being considered as an alternative solution for real-time communication (compared to traditional fieldbusses). Although Ethernet was originally not intended for this purpose, the development of the Ethernet AVB standard enables its use for transporting high-volume data (e.g. from cameras and entertainment applications) with low-latency guarantees. In complex industrial systems, the network is shared by many applications, developed by different parties. To face this complexity, the development of these applications must be kept as independent as possible. In particular, from a network point of view, progress of all communication streams must be guaranteed, and the performance for individual streams should be predictable using only information regarding the stream under study and the general parameters of the communication standard used by the network. Initial methods to guarantee latency for Ethernet AVB networks rely on the traditional busy-period analysis. Typically, these methods are based on knowledge of the inter-arrival patterns of both the stream under study and the interfering streams that also traverse the network. The desired independence is therefore not achieved. In this paper, we present an independent real-time analysis based on so-called eligible intervals, which does not rely on any assumptions on interfering priority classes other than those enforced in the Ethernet AVB standard. We prove this analysis is tight in case there is only a single higher-priority stream, and no additional information on interference is known. In case there are multiple higher-priority streams, we give conditions under which the analysis is still tight. Furthermore, we compare the results of our approach to the two most recent busy-period analyses, point out sources of pessimism in these earlier works, and argue that assuming more information on the sources of interference (e.g. a minimal inter-arrival time between interfering frames) has only limited advantages.

Highlights

  • The increasing use of, for example, cameras in industrial systems, is resulting in an increasing demand for high-volume data transports

  • Ethernet was not intended for this purpose, but the development of the Ethernet AVB standard [from the audio/video bridging task group IEEE (2005)], enables the use of Ethernet for transporting high-volume data with latency guarantees

  • We improved our work of Cao et al (2016a, b), extending its application to the case where there are multiple higher and lower priority classes that interfere with the transmission of a frame

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Summary

Introduction

The increasing use of, for example, cameras in industrial systems, is resulting in an increasing demand for high-volume data transports. Within class H and M, a stream of frames is shaped according to the creditbased shaping algorithm, constraining the transmission for those classes to a fraction of the available bandwidth This prevents starvation of the low priority classes, and in particular enables us to ensure latency guarantees for the M class, which is the main focus of this paper. The problem with using a busy period analysis for the analysis of Ethernet AVB, is that this method was originally developed for non-idling servers This concern is not explicitly addressed in the works of Diemer et al (2012b), Axer et al (2014) and Bordoloi et al (2014), but it is implicitly solved by adding the idling time as additional interference.

Related work
System model
Mechanics of credit-based shaping
Notation
Formalization of shaping rules
Analysis
Shaping a single un-interfered stream
The start of an eligible interval
Credit represents delay
Bounding the credit build up by interference
Bounding the delay caused by interference
An example of relative worst-case response time
Tightness
Tightness for interference by a single higher priority class
Examples why tightness is not always guaranteed
Tightness for interference of multiple higher priority classes
Comparison with earlier work
Illustrative example given a single high priority
C Ci Ti Di
Exploring pessimism
Adding information
Findings
Conclusions and future work
Full Text
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