No abstract

Consolidating tasks to a smaller number of electronic control units (ECUs) is an important strategy for optimizing costs and resources in the automotive industry. In our research, we aim to enable ECU consolidation by migrating tasks at runtime between different ECUs, which adds redundancy and fail-safety capabilities to the system. In this paper, we present a setup with a generalistic and modular architecture that allows for integrating and testing different ECU architectures and machine learning (ML) models. As part of a holistic testbed, we introduce a collection of reproducible tasks, as well as a toolchain that controls the dynamic migration of tasks depending on ECU status and load. The migration is aided by the machine learning predictions on the schedulability analysis of possible future task distributions. To demonstrate the capabilities of the setup, we show its integration with FreeRTOS-based ECUs and two ML models—a long short-term memory (LSTM) network and a spiking neural network—along with a collection of tasks to distribute among the ECUs. Our approach shows a promising potential for machine-learning-based schedulability analysis and enables a comparison between different ML models.