Automotive ECUs have been upgraded with multicore processor implementation. It has the benefits of achieving high computing power without increasing the clock speed. System developers partition the automotive application tasks to have parallelizability and avoid interference between various software modules. Task intensive applications are assigned to multiple CPU cores. To improve the performance of such systems, there has to be an efficient task scheduler. In this regard, the Automotive Open System Architecture (AUTOSAR) suggests partitioned static priority scheduling for parallelized software for the multicore ECUs. In this approach, the difficulty lies with task clustering and partitioning for specific cores. There is no exact criterion to be followed to partition the tasks. Due to which cores are not balanced with loads. Under contingency conditions, there are chances of tasks missing deadlines. This paper addresses this issue by exploring a mixed approach scheduling algorithm which has features of both static and dynamic scheduling and also few adaptations of partitioned and global scheduling. With this algorithm, high load conditions under contingency consequences are tested. This algorithm was run and tested using a scheduling simulator with real time task models of periodic tasks, angle synchronous interrupts and event triggered interrupts. The performance parameters considered here are, the % of core utilization, response time, deadlines missing rate. It has been verified that, this proposed algorithm is able to find a feasible schedule under various contingency scenarios and contributes to improve the safety level of the vehicle.

ECU Multicore RTOS Scheduling OEM AUTOSAR.