AUTOSAR Runnable Periods Optimization for DAG-Based Complex Automobile Applications
Source abstract
Abstract
When developing an automobile control application, its scheduling parameters as well as the control algorithm itself should be carefully optimized to achieve the best control performance from given computing resources. Moreover, since the wide acceptance of the AUTOSAR standard, where finer-granular scheduling entities (called runnables) rather than the traditional real-time tasks are used, the number of scheduling parameters to be optimized is far greater than the traditional task-based control systems. Hence, due to the vast problem space, it is not feasible to reuse existing time-consuming search-based optimization methods. With this motivation, this paper presents an analytical codesign method for deciding runnable periods that minimize given control cost functions. Our solution approach, based on the Lagrange multiplier method, can find optimized runnable periods in polynomial times due to its analytical nature. Moreover, our evaluation results for synthesized applications with varying complexities show that our method performs significantly better (12% to 59% of control cost reductions) than a state-of-the-art evolutionary algorithm. To the best of our knowledge, this study is one of the first attempts to find runnable periods that maximize a given system’s control performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Real-Time Systems Scheduling · Embedded Systems Design Techniques · Formal Methods in Verification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Mobility Business Division, SWM.AI, Anyang-si, Gyeonggi-do 14055, Korea
Graduate School of Automotive Engineering, Kookmin University, Seoul 02707, Korea
Department of Automobile and IT Convergence, Kookmin University, Seoul 02707, Korea
References (40)
Show source-provided references
1. Kehr, S., Panić, M., Quiñones, E., Böddeker, B., Sandoval, J.B., Abella, J., Cazorla, F.J., and Schäfer, G. (2016, January 14–18). Supertask: Maximizing runnable-level parallelism in AUTOSAR applications. Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden, Germany.10.3850/9783981537079_0078 ↗
2. Kim, J.W., Lee, K.J., and Ahn, H.S. (2015, January 13–16). Development of software component architecture for motor-driven power steering control system using AUTOSAR methodology. Proceedings of the 2015 15th International Conference on Control, Automation and Systems (ICCAS), Busan, Korea.10.1109/ICCAS.2015.7364695 ↗
3. Park, J., and Choi, B.W. (2019). Design and implementation procedure for an advanced driver assistance system based on an open source AUTOSAR. Electronics, 8.10.3390/electronics8091025 ↗
4. AUTOSAR (2020, July 13). Recommended Methods and Practices for Timing Analysis and Design within the AUTOSAR Development Process. AUTOSAR Classic Platform 4.3.1. Available online: https://www.autosar.org/fileadmin/user_upload/standards/classic/4-3/AUTOSAR_TR_TimingAnalysis.pdf.
5. Kim, T.W., Lee, G.M., and Kim, J.C. (2018, January 17–20). AUTOSAR Runnable Scheduling for Optimal Tradeoff between Control Performance and CPU Utilization. Proceedings of the 2018 18th International Conference on Control, Automation and Systems (ICCAS), PyeongChang, Korea.
6. Bini, E., and Cervin, A. (December, January 30). Delay-aware period assignment in control systems. Proceedings of the 2008 Real-Time Systems Symposium, Barcelona, Spain.10.1109/RTSS.2008.45 ↗
7. Wu, Y., Bini, E., and Buttazzo, G. (2008, January 25–27). A framework for designing embedded real-time controllers. Proceedings of the 2008 14th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, Kaohisung, Taiwan.10.1109/RTCSA.2008.22 ↗
8. Parameter selection for real-time controllers in resource-constrained systems10.1109/TII.2010.2053378 ↗
9. A heuristic task periods selection algorithm for real-time control systems on a multi-core processor10.1109/ACCESS.2017.2768559 ↗
10. Tan, L., Du, C., and Dong, Y. (June, January 31). Control-performance-driven period and deadline selection for cyber-physical systems. Proceedings of the 2015 10th Asian Control Conference (ASCC), Kota Kinabalu, Malaysia.
11. Scheduling algorithms for multiprogramming in a hard-real-time environment10.1145/321738.321743 ↗
12. Seto, D., Lehoczky, J.P., Sha, L., and Shin, K.G. (1996, January 4–6). On task schedulability in real-time control systems. Proceedings of the 17th IEEE Real-Time Systems Symposium, Washington, DC, USA.
13. Seto, D., Lehoczky, J.P., and Sha, L. (1998, January 2–4). Task period selection and schedulability in real-time systems. Proceedings of the 19th IEEE Real-Time Systems Symposium (Cat. No. 98CB36279), Madrid, Spain.
14. Enrico Bini, M.D.N. (2005, January 6–8). Optimal task rate selection in fixed priority systems. Proceedings of the 26th IEEE International Real-Time Systems Symposium (RTSS’05), Miami, FL, USA.
15. Period selection for integrated controller tasks in cyber-physical systems10.1016/j.cja.2015.04.011 ↗
16. Xu, Y., Cervin, A., and Årzén, K.E. (2016, January 17–19). Harmonic scheduling and control co-design. Proceedings of the 2016 IEEE 22nd International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), Daegu, Korea.10.1109/RTCSA.2016.44 ↗
17. Control-scheduling codesign exploiting trade-off between task periods and deadlines
18. Davare, A., Zhu, Q., Di Natale, M., Pinello, C., Kanajan, S., and Sangiovanni-Vincentelli, A. (2007, January 4–8). Period optimization for hard real-time distributed automotive systems. Proceedings of the 44th annual Design Automation Conference, San Diego, CA, USA.10.1109/DAC.2007.375172 ↗
19. Feiertag, N., Richter, K., Nordlander, J., and Jonsson, J. (December, January 30). A compositional framework for end-to-end path delay calculation of automotive systems under different path semantics. Proceedings of the IEEE Real-Time Systems Symposium, Washington, DC, USA.
20. Schlatow, J., Mostl, M., Tobuschat, S., Ishigooka, T., and Ernst, R. (2018, January 6–8). Data-age analysis and optimisation for cause-effect chains in automotive control systems. Proceedings of the 2018 IEEE 13th International Symposium on Industrial Embedded Systems (SIES), Graz, Austria.10.1109/SIES.2018.8442077 ↗
21. End-to-End Timing Analysis of Sporadic Cause-Effect Chains in Distributed Systems
22. Choi, J., Kang, D., and Ha, S. (2018, January 19–23). End-to-end latency analysis of cause-effect chains in an engine management system. Proceedings of the 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden, Germany.10.23919/DATE.2018.8342196 ↗
23. Long, R., Li, H., Peng, W., Zhang, Y., and Zhao, M. (2009, January 25–27). An approach to optimize intra-ecu communication based on mapping of autosar runnable entities. Proceedings of the 2009 International Conference on Embedded Software and Systems, Hangzhou, China.10.1109/ICESS.2009.63 ↗
24. Multisource software on multicore automotive ECUs—Combining runnable sequencing with task scheduling10.1109/TIE.2012.2185913 ↗
25. Saidi, S.E., Cotard, S., Chaaban, K., and Marteil, K. (2015, January 21). An ILP approach for mapping autosar runnables on multi-core architectures. Proceedings of the 2015 Workshop on Rapid Simulation and Performance Evaluation: Methods and Tools, Amsterdam, The Netherlands.10.1145/2693433.2693439 ↗
26. Kehr, S., Quiñones, E., Langen, D., Böddeker, B., and Schäfer, G. (2017, January 18–21). Parcus: Energy-aware and robust parallelization of AUTOSAR legacy applications. Proceedings of the 2017 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), Pittsburg, PA, USA.10.1109/RTAS.2017.4 ↗
27. Control Performance-aware AUTOSAR Runnable Scheduling10.7467/KSAE.2020.28.1.009 ↗
28. From a federated to an integrated automotive architecture10.1109/TCAD.2009.2014005 ↗
29. Moving from federated to integrated architectures in automotive: The role of standards, methods and tools10.1109/JPROC.2009.2039550 ↗
30. AUTOSAR (2020, July 13). Virtual Function Bus. AUTOSAR Classic Platform 4.3.1. Available online: https://www.autosar.org/fileadmin/user_upload/standards/classic/4-3/AUTOSAR_EXP_VFB.pdf.
31. How does control timing affect performance? Analysis and simulation of timing using Jitterbug and TrueTime10.1109/MCS.2003.1200240 ↗
32. AUTOSAR (2020, July 13). Specification of Timing Extensions. AUTOSAR Classic Platform 4.3.1. Available online: https://www.autosar.org/fileadmin/user_upload/standards/classic/4-3/AUTOSAR_TPS_TimingExtensions.pdf.
33. Peraldi-Frati, M.A., Blom, H., Karlsson, D., and Kuntz, S. (2012, January 12–16). Timing modeling with autosar-current state and future directions. Proceedings of the 2012 Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden, Germany.10.1109/DATE.2012.6176604 ↗
34. Lincoln, B., and Cervin, A. (2002, January 10–13). Jitterbug: A tool for analysis of real-time control performance. Proceedings of the 41st IEEE Conference on Decision and Control, Las Vegas, NV, USA.
35. TrueTime: Simulation of control loops under shared computer resources10.3182/20020721-6-ES-1901.00975 ↗
36. Yu, F., Li, D.F., and Crolla, D. (2008, January 3–5). Integrated vehicle dynamics control—State-of-the art review. Proceedings of the 2008 IEEE Vehicle Power and Propulsion Conference, Harbin, China.
37. Vehicle dynamics integrated control for four-wheel-distributed steering and four-wheel-distributed traction/braking systems10.1080/00423110500385790 ↗
38. Akiyama, S., and Tashiro, T. (2006). Integrated Vehicle Control System. (7,047,117), U.S. Patent.
39. Kennedy, J., and Eberhart, R. (1995, January 17–19). Particle swarm optimization. Proceedings of the ICNN’95-International Conference on Neural Networks, Munich, Germany.
40. Miranda, L.J.V. (2020, July 13). PySwarms a Research Toolkit for Particle Swarm Optimization in Python. Available online: https://pyswarms.readthedocs.io/en/latest/index.html.