Pre-Shaping Bursty Transmissions under IEEE802.1Q as a Simple and Efficient QoS Mechanism

International audienceThe automotive industry is swiftly moving towards Ethernet as the high-speed communication network for in-vehicle communication. There is nonetheless a need for protocols that go beyond what standard Ethernet has to offer in order to provide additional QoS to demanding applications such as ADAS systems or audio/video streaming. The main protocols currently considered for that purpose are IEEE802.1Q, AVB with the Credit Based Shaper mechanism (IEEE802.1Qav) and TSN with its Time-Aware Shaper (IEEE802.1Qbv). AVB/CBS and TSN/TAS both provide efficient QoS mechanisms and they can be used in a combined manner, which offers many possibilities to the designer. Their use however requires dedicated hardware and software components, and clock synchronization in the case of TAS. Previous studies have also shown that the efficiency of these protocols depends much on the application at hand and the value of the configuration parameters. In this work, we explore the use of "pre-shaping" strategies under IEEE802.1Q for bursty traffic such as audio/video streams as a simple and efficient alternative to AVB/CBS and TSN/TAS. Pre-shaping means inserting on the sender side "well-chosen" pauses between successive frames of a burst (e.g., a camera frame), all the other characteristics of traffic remaining unchanged. We show on an automotive case-study how the use of pre-shaping for audio/video streams leads to a drastic reduction of the communication latencies for the best-effort streams while enabling to meet the timing constraints for the rest of the traffic. We then discuss the limitations of the pre-shaping mechanism and future works needed to facilitate its adoption

Insights on the Performance and Configuration of AVB and TSN in Automotive Ethernet Networks

Switched Ethernet is profoundly reshaping in-car communications. To meet the diverse real-time requirements in automotive communications, Quality-of-Service protocols that go beyond the mere use of priorities are required. In this work, the basic questions that we investigate on a case-study with diverse and demanding communication requirements is what can we expect from the various protocols aimed at providing a better timing Quality of Service on top of Ethernet? And how to use them? Especially how to use them in a combined manner. We will focus on the Credit-Based Shaper of AVB, the Time-Aware Shaper of TSN and the use of priorities as defined in IEEE802.1Q. The performance metrics considered are the distributions of the communication latencies, obtained by simulation, as well as upper bounds on these quantities obtained by worst-case schedulability analysis. If there have been over the last 5 years numerous studies on the performance of AVB CBS, the literature on comparing AVB to TSN and other candidate protocols is still sparse. To the best of our knowledge, this empirical study is the first to consider most protocols currently considered in the automotive domain, with the aim to gain insights into the different technological, design and configurations alternatives. In particular, an objective of this study is to identify key problems that need to be solved in order to further automate network design and configuration

Using Machine Learning to Speed Up the Design Space Exploration of Ethernet TSN networks

In this work, we ask if Machine Learning (ML) can provide a viable alternative to conventional schedulability analysis to determine whether a real-time Ethernet network meets a set of timing constraints. Otherwise said, can an algorithm learn what makes it difficult for a system to be feasible and predict whether a configuration will be feasible without executing a schedulability analysis? In this study, we apply standard supervised and unsupervised ML techniques and compare them, in terms of their accuracy and running times, with precise and approximate schedulability analyses in Network-Calculus. We show that ML techniques are efficient at predicting the feasibility of realistic TSN networks and offer new trade-offs between accuracy and computation time especially interesting for design-space exploration algorithms