An Architecture to Enable Machine-Learning-Based Task Migration for Multi-Core Real-Time Systems

ECU consolidation is an automotive trend that tends to reduce the number of electronic devices in a vehicle to optimize resources and costs. However, its implementation introduces new challenges, especially in terms of safety. Research at our group is exploring the idea of task migration between different electronic-control-units (ECUs) to add redundancy and fail-safety capabilities to an automotive setup. In particular, we are exploring machine-learning-aided schedulability analysis strategies as means to decide which ECU a task should be mapped to. In this paper. we present the implementation of an architecture that allows for testing different machine-learning techniques for schedulability analysis, enabling the deployment of tasks to the respective ECUs and a simple migration of tasks between them. The architecture is based on a real-time operating system. The test system developed implements a mix of dummy tasks with constant execution times and an autonomous task that interacts with a virtual environment and with a variable execution time. Also, the architecture allows for collecting data on each task for proving if the executed task sets are actually schedulable, as predicted by the machine learning component.