Performance optimization of task intensive real‐time applications on multicore ECUs—A hybrid scheduler

Abstract

In the current approach of automotive electronic system design, the multicore processors have prevailed to achieve high computing performance at low thermal dissipation. Multicore processors offer functional parallelism that helps in meeting the safety critical requirements of vehicles. The number of Electronic Control Units (ECUs) in high-end cars could be reduced by conglomerating more functions into a multicore ECU. AUTOSAR stack has been designed to support the applications developed for multicore ECUs. The real challenges lie in adapting new design methods while developing sophisticated applications with multicore constraints. It is imperative to utilize the most of multicore computational capability towards enhancing the overall performance of ECUs. In this context, the scheduling of the real-time multitasking software components by the operating system is one of the challenging issues to be addressed. Here, the state-of-the-art scheduling algorithm is reviewed and its merits and limitations are identified. A hybrid scheduler has been proposed, tested and compared with the state-of-the-art algorithm that offers better performance in terms of CPU utilization, average response time and deadline missing rate both in normal and high load conditions.

The evolution of embedded systems in automotive industry: A global review

“…The migration of engine ECU software from single-core to multi-core architectures has been a significant development, requiring new methodologies to accommodate these changes (Poon, 2022). Furthermore, the prevalence of multicore processors has enabled high computing performance with low thermal dissipation, contributing to the optimization of task-intensive real-time applications in automotive electronic system design (Mishra & Hegde, 2021). Additionally, the development of open-source-based peripherals for automotive electronic control units has provided educational platforms for understanding the functionalities of these systems (Pancik & Beneš, 2018).…”