Towards Efficient Multi-Frame Clustering in Response Time Analysis for Large Object Communication

In autonomous systems, growing sizes of application data, primarily related to perception tasks, have to be transmitted over communication infrastructures that provide higher data rates. Knowledge and exploitation of the clustered structure of multi-frame application data have proven to reduce the interference between different real-time critical communication streams, as recently shown for synchronous systems. However, the accompanying increase in frame numbers will foreseeably make the corresponding analysis impractical due to frame-dependent processing times. As a solution, based on the synchronous example mentioned, we demonstrate how to compose multi-frame transmissions to larger frame clusters in the analysis and decouple the analysis complexity from the application data sizes. Based on an automotive and industrial TSN use case, our results show comparable analytical response times and very low computation times at arbitrary data rates and sizes. It thus enables the deployment of efficient configuration methods that arbitrate large data samples transmitted through the network as one whole frame cluster. In addition, we made the developed analysis openly accessible.