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Article

A Generalistic Approach to Machine-Learning-Supported Task Migration on Real-Time Systems †

1
Fortiss GmbH, Research Institute of the Free State of Bavaria, 80805 Munich, Germany
2
Department of Informatics, Technical University of Munich, 85748 Garching bei München, Germany
*
Authors to whom correspondence should be addressed.
This paper is an extended version of our paper published in MCSoC 2021: Delgadillo, O.; Blieninger, B.; Kuhn, J.; Baumgarten, U. “An Architecture to Enable Machine-Learning-Based Task Migration for Multi-Core Real-Time Systems”. In Proceedings of the 2021 IEEE 14th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), Singapore, 20–23 December 2021; pp. 405–412. doi:10.1109/MCSoC51149.2021.00066.
Academic Editor: Weidong Kuang
J. Low Power Electron. Appl. 2022, 12(2), 26; https://doi.org/10.3390/jlpea12020026
Received: 17 March 2022 / Revised: 17 April 2022 / Accepted: 28 April 2022 / Published: 3 May 2022
(This article belongs to the Special Issue Low Power AI)
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. View Full-Text
Keywords: task migration; real-time; ECU consolidation; RTOS; spiking neural network task migration; real-time; ECU consolidation; RTOS; spiking neural network
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MDPI and ACS Style

Delgadillo, O.; Blieninger, B.; Kuhn, J.; Baumgarten, U. A Generalistic Approach to Machine-Learning-Supported Task Migration on Real-Time Systems. J. Low Power Electron. Appl. 2022, 12, 26. https://doi.org/10.3390/jlpea12020026

AMA Style

Delgadillo O, Blieninger B, Kuhn J, Baumgarten U. A Generalistic Approach to Machine-Learning-Supported Task Migration on Real-Time Systems. Journal of Low Power Electronics and Applications. 2022; 12(2):26. https://doi.org/10.3390/jlpea12020026

Chicago/Turabian Style

Delgadillo, Octavio, Bernhard Blieninger, Juri Kuhn, and Uwe Baumgarten. 2022. "A Generalistic Approach to Machine-Learning-Supported Task Migration on Real-Time Systems" Journal of Low Power Electronics and Applications 12, no. 2: 26. https://doi.org/10.3390/jlpea12020026

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