Time-Triggered Ethernet

Scheible, Guntram; Dzung, Dacfey; Endresen, Jan; Frey, J.-E.

doi:10.1201/b17365-44

Cited by 8 publications

(13 citation statements)

References 2 publications

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“…Intuitively one would expect that the values differ more from the real period as the utilization of the network increases. But it is important to remember that the period is calculated using equation (5). There, in the numerator is the sum of the interarrival times of the frames to the switch.…”

Section: Resultsmentioning

confidence: 99%

“…This is especially true for time-triggered networks, that provide deterministic guarantees by scheduling the moments in time in which messages are sent. The schedule is obtained off-line and all the information about the network topology and the application requirements must be known beforehand [4] [5]. Thus, these characteristics limit the configuration flexibility of a time-triggered network.…”

Section: Introductionmentioning

confidence: 99%

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Learning the parameters of periodic traffic based on network measurements

Gutiérrez

Steiner

Dobrin

et al. 2015

2015 IEEE International Workshop on Measurements &Amp; Networking (M&N)

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The configuration of real-time networks is one of the most challenging demands of the Real-Time Internet-of-Things trend, where the network has to be deterministic and yet flexible enough to adapt to changes through its life-cycle. To achieve this we have outlined an approach that learns the necessary configuration parameters from network measurements, that way providing a continuous configuration service for the network. First, the network is monitored to obtain traffic measurements. Then traffic parameters are derived from those measurements. Finally, a new time-triggered schedule is produced with which the network will be reconfigured.In this paper we propose an analysis based on measurements to obtain the specific traffic parameters and we evaluate it through network simulations. The results show that the configuration parameters can be learned from the measurements with enough accuracy and that those measurements can be easily obtained through network monitoring.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning the parameters of periodic traffic based on network measurements

Gutiérrez

Steiner

Dobrin

et al. 2015

2015 IEEE International Workshop on Measurements &Amp; Networking (M&N)

Self Cite

View full text Add to dashboard Cite

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“…In Figure 1(b), two applications with periods 5 and 15 respectively comprise tasks a 1 , a 4 , a 5 , a 8 and a 9 that are mapped on to ECUs 1, 2, and 3. The tasks communicate via messages a 2 , a 3 , a 6 , and a 7 over the links of a switched time-triggered network [36]. As shown in Figure 1(c), the tasks executing on the ECUs are scheduled with zero jitter, whereas the messages on the network links have non-zero jitter.…”

Section: B Application Modelmentioning

confidence: 99%

“…We assume a time-triggered Ethernet with a bandwidth of 100 Mbps [36]. For messages that transmit video content, we set a maximum desirable utilization of one video message in its period to 0.1 to still be schedulable, while reflecting typical message sizes.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…To reduce cost, automotive applications typically share platform resources on which they are executed, e.g., ECUs and network links [36]. Therefore, car manufacturers face the problem of integrating subsystems from different suppliers comprising one or multiple applications, such that their control and timing requirements are guaranteed [22], [24], [39].…”

Section: Introductionmentioning

confidence: 99%

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Control Performance Optimization for Application Integration on Automotive Architectures

Minaeva

Roy

Åkesson

et al. 2021

IEEE Trans. Comput.

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Automotive software implements different functionalities as multiple control applications sharing common platform resources. Although such applications are often developed independently, the control performance of the resulting system depends on how these applications are integrated. A key integration challenge is to efficiently schedule these applications on shared resources with minimal control performance degradation. We formulate this problem as that of scheduling multiple distributed periodic control tasks that communicate via messages with non-zero jitter. The optimization criterion used is a piecewise linear representation of the control performance degradation as a function of the end-to-end latency of the application. The three main contributions of this article are: 1) a constraint programming (CP) formulation to solve this integration problem optimally on time-triggered architectures, 2) an efficient heuristic called Flexi, and 3) an experimental evaluation of the scalability and efficiency of the proposed approaches. In contrast to the CP formulation, which for many real-life problems might have unacceptably long running times, Flexi returns nearly optimal results (0.5% loss in control performance compared to optimal) for most problems with more acceptable running times.

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Analysis of Synchronization in Distributed Avionics Systems Based on Time-Triggered Ethernet

Tariq

Petrunin

Al‐Rubaye

2021

2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)

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Significant developments are made in unmanned aerial vehicles (UAVs) and in avionics, where messages sent in the network with critical Time play a vital role. Several studies in Time-Triggered Ethernet have been carried out, but these studies. Still, these improving QoS such as latency in end-to-end delays of an internally synchronized TTE network. However, no one monitors integrated modular avionics. We proposed a framework that enables TTE to be externally synchronized from a GNSS to overcome this problem. We have incorporated our proposed Algorithm in the TTE protocol based on specific parameters and multiple existing algorithms. The proposed Algorithm gives us the ability to control and synchronize the TTE network. Also, we have a developed scenario for analyzing the performance of externally synchronized end-to-end latency of TT messages in a TTE network.We simulated scenarios in our framework and analyzed QoS but, more specifically, the latency that affects the performance of time-triggered messages in externally synchronized TTE networks. The result shows that our proposed framework outperforms existing approaches.

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Time-Triggered Ethernet

Cited by 8 publications

References 2 publications

Learning the parameters of periodic traffic based on network measurements

Learning the parameters of periodic traffic based on network measurements

Control Performance Optimization for Application Integration on Automotive Architectures

Analysis of Synchronization in Distributed Avionics Systems Based on Time-Triggered Ethernet

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