Deadline Miss Early Detection Method for DAG Tasks Considering Variable Execution Time

Authors Hayate Toba , Takuya Azumi



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Author Details

Hayate Toba
  • Graduate School of Science and Engineering, Saitama University, Japan
Takuya Azumi
  • Graduate School of Science and Engineering, Saitama University, Japan

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Hayate Toba and Takuya Azumi. Deadline Miss Early Detection Method for DAG Tasks Considering Variable Execution Time. In 36th Euromicro Conference on Real-Time Systems (ECRTS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 298, pp. 8:1-8:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.ECRTS.2024.8

Abstract

Autonomous driving systems must guarantee safety, which requires strict real-time performance. A series of processes, from sensor data input to vehicle control command output, must be completed by the end-to-end deadline. If a deadline miss occurs, the system must quickly transition to a safe state. To improve safety, an early detection method for deadline misses was proposed. The proposed method represents the autonomous driving system as a directed acyclic graph (DAG) with a mixture of timer-driven and event-driven nodes. It assigns appropriate time constraints for each node based on the end-to-end deadline. However, the existing methods assume the worst-case execution time (WCET) for calculating the time constraints of each node and do not consider the execution time variation of nodes, making the detection of deadline misses pessimistic. This paper proposes a deadline miss early detection method to determine the possibility of deadline misses quantitatively at the beginning of each node execution in a DAG task. It calculates the time constraints of each node using probabilistic execution time, which treats execution time as a random variable. Experimental evaluation shows that the proposed method reduces pessimism, which is a problem of conventional methods using WCET, and then achieves more accurate early detection of deadline misses. The evaluation also indicates that the execution time of static analysis required for deadline miss early detection is within a practical level.

Subject Classification

ACM Subject Classification
  • Computer systems organization → Real-time systems
Keywords
  • Autonomous driving system
  • deadline miss early detection
  • DAG
  • event-driven task
  • timer-driven task
  • probabilistic execution time

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References

  1. Baidu Apollo project. URL: https://www.apollo.auto/.
  2. Matthias Becker, Dakshina Dasari, Saad Mubeen, Moris Behnam, and Thomas Nolte. Synthesizing job-level dependencies for automotive multi-rate effect chains. In Proceedings of the 22nd IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), pages 159-169, 2016. Google Scholar
  3. Guillem Bernat, Antoine Colin, and Stefan M. Petters. WCET analysis of probabilistic hard real-time systems. In Proceedings of the 23rd IEEE Real-Time Systems Symposium (RTSS), pages 279-288, 2002. Google Scholar
  4. Daniel Casini, Tobias Blaß, Ingo Lütkebohle, and Björn B. Brandenburg. Response-Time Analysis of ROS 2 Processing Chains Under Reservation-Based Scheduling. In Proceedings of the 21st Euromicro Conference on Real-Time Systems (ECRTS), pages 1-23, 2019. Google Scholar
  5. José Luis Díaz, José María López, Manuel García, Antonio Manuel Campos, Kanghee Kim, and Lucia Lo Bello. Pessimism in the stochastic analysis of real-time systems: concept and applications. In Proceedings of the 25th IEEE International Real-Time Systems Symposium (RTSS), pages 197-207, 2004. Google Scholar
  6. Taeho Han and Kanghee Kim. Minimizing probabilistic end-to-end latencies of autonomous driving systems. In Proceedings of the 29th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pages 27-39, 2023. Google Scholar
  7. Shinpei Kato, Shota Tokunaga, Yuya Maruyama, Seiya Maeda, Manato Hirabayashi, Yuki Kitsukawa, Abraham Monrroy, Tomohito Ando, Yusuke Fujii, and Takuya Azumi. Autoware on board: Enabling autonomous vehicles with embedded systems. In Proceedings of the 9th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), pages 287-296, 2018. Google Scholar
  8. Alix Munier Kordon and Ning Tang. Evaluation of the age latency of a real-time communicating system using the LET paradigm. In Proceedings of the 32nd Euromicro Conference on Real-Time Systems (ECRTS), pages 1-21, 2020. Google Scholar
  9. Takahisa Kuboichi, Atsushi Hasegawa, Bo Peng, Keita Miura, Kenji Funaoka, Shinpei Kato, and Takuya Azumi. CARET: Chain-Aware ROS 2 Evaluation Tool. In Proceedings of the 20th IEEE International Conference on Embedded and Ubiquitous Computing (EUC), pages 1-8, 2022. Google Scholar
  10. Hyoeun Lee, Youngjoon Choi, Taeho Han, and Kanghee Kim. Probabilistically guaranteeing end-to-end latencies in autonomous vehicle computing systems. IEEE Transactions on Computers, 71(12):3361-3374, 2022. Google Scholar
  11. Jing Li, Jian Jia Chen, Kunal Agrawal, Chenyang Lu, Chris Gill, and Abusayeed Saifullah. Analysis of federated and global scheduling for parallel real-time tasks. In Proceedings of the 26th Euromicro Conference on Real-Time Systems (ECRTS), pages 85-96, 2014. Google Scholar
  12. Filip Marković, Alessandro Vittorio Papadopoulos, and Thomas Nolte. On the Convolution Efficiency for Probabilistic Analysis of Real-Time Systems. In Proceedings of the 33rd Euromicro Conference on Real-Time Systems (ECRTS), pages 16:1-16:22, 2021. Google Scholar
  13. Dorin Maxim, Mike Houston, Luca Santinelli, Guillem Bernat, Robert I. Davis, and Liliana Cucu-Grosjean. Re-sampling for statistical timing analysis of real-time systems. In Proceedings of the 20th ACM International Conference on Real-Time and Network Systems (RTNS), pages 111-120, 2012. Google Scholar
  14. Suzana Milutinovic, Jaume Abella, Damien Hardy, Eduardo Quiñones, Isabelle Puaut, and Francisco J. Cazorla. Speeding up static probabilistic timing analysis. In Proceedings of the 28th International Conference on Architecture of Computing Systems (ARCS), pages 236-247, 2015. Google Scholar
  15. SAE International. Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles, 4th edition, April 2021. Google Scholar
  16. John A Stankovic, Marco Spuri, Krithi Ramamritham, and Giorgio Buttazzo. Deadline scheduling for real-time systems: EDF and related algorithms, volume 460. Springer Science & Business Media, 1998. Google Scholar
  17. Micaela Verucchi, Mirco Theile, Marco Caccamo, and Marko Bertogna. Latency-aware generation of single-rate DAGs from multi-rate task sets. In Proceedings of the 26th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pages 226-238, 2020. Google Scholar
  18. Waymo driver. URL: https://waymo.com/waymo-driver/.
  19. Atsushi Yano and Takuya Azumi. Deadline miss early detection method for mixed timer-driven and event-driven DAG tasks. IEEE Access, 11:22187-22200, 2023. Google Scholar
  20. Atsushi Yano and Takuya Azumi. RD-Gen: Random DAG generator considering multi-rate applications for reproducible scheduling evaluation. In Proceedings of the 26th IEEE International Symposium on Real-Time Distributed Computing (ISORC), pages 21-31, 2023. Google Scholar