Document Open Access Logo

Task-To-Processor Assignment for Real-Time Mixed-Critical Networked Systems Using Inductive Logic Programming

Authors Marcus Gualtieri , Christian Juette , Dakshina Dasari

PDF HTML 5

Files

Thumbnail PDF
PDF
LIPIcs.ECRTS.2025.14.pdf
  • Filesize: 2 MB
  • 26 pages
HTML5 Logo HTML (experimental)
LIPIcs.ECRTS.2025.14.html

Document Identifiers

Subject Classification

ACM Subject Classification
  • Computer systems organization → Real-time systems
  • Computer systems organization → Embedded systems
  • Networks → End nodes
Keywords
  • Real-Time Distributed Systems
  • Auto-Configuration
  • Task-to-Processor Mapping
  • Inductive Logic Programming
  • Active Learning

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Abstract

Task-to-processor assignment is an essential aspect of configuring real-time, distributed systems, since an improper assignment can adversely affect latency. Model-based, heuristic, and data-driven approaches have been proposed to solve the task-to-processor assignment problem. However, model-based and heuristic approaches require revision if the system changes, and data-driven approaches require training on a lot of data and setting nonintuitive hyper-parameters.
We explore a hybrid approach which takes both a system description and data: we use inductive logic programming in an active learning algorithm to search for assignments which satisfy a real-time requirement. By using both domain knowledge and data, the system finds solutions quickly, and changes are not required when using the tool on different systems. Furthermore, the output is a human-readable description of a set of predicted satisfactory assignments. Readable solution sets are useful for analyzing the system, since we can easily compare solution sets across different setups.
We evaluate our approach on real systems with mixed-critical network flows. We show that task-to-processor assignment can significantly influence latency by comparing optimal fixed assignments to the default Linux scheduler. We show that our approach finds assignments that are within 10% of optimal with up to 10× fewer system tests, compared to random search. Our algorithm also performs favorably to load balancing and neural network baselines.

Cite As Get BibTex

Marcus Gualtieri, Christian Juette, and Dakshina Dasari. Task-To-Processor Assignment for Real-Time Mixed-Critical Networked Systems Using Inductive Logic Programming. In 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 335, pp. 14:1-14:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.ECRTS.2025.14

Author Details

Marcus Gualtieri
  • Robert Bosch LLC, Corporate Research, Sunnyvale, CA, USA
Christian Juette
  • Robert Bosch LLC, Corporate Research, Sunnyvale, CA, USA
Dakshina Dasari
  • Robert Bosch GmbH, Corporate Research, Renningen, Germany

Acknowledgements

We are grateful to Liu Ren and Philip Mundhenk, for supporting this project, Michael Wang, for discussing this paper, to Andrew Cropper, for answering questions about running Popper, and to the anonymous reviewers, for giving suggestions which improved the paper.

References

  1. Liang Bao, Xin Liu, Ziheng Xu, and Baoyin Fang. AutoConfig: Automatic configuration tuning for distributed message systems. In ACM/IEEE International Conference on Automated Software Engineering, pages 29-40, 2018. URL: https://doi.org/10.1145/3238147.3238175.
  2. Rudy Belliardi, Dorr Josef, Thomas Enzinger, Florian Essler, Janós Farkas, Hantel Mark, Maximilian Riegel, Marius-Petru Stanica, Guenter Steindl, Reiner Wamßer, Karl Weber, and Steven Zuponcic. Use cases IEC/IEEE 60802. Technical Report V1.3, IEEE, 2018. Google Scholar
  3. Bokhari. On the mapping problem. IEEE Transactions on Computers, 100(3):207-214, 1981. URL: https://doi.org/10.1109/TC.1981.1675756.
  4. Christopher Bryant, Stephen Muggleton, Douglas Kell, Philip Reiser, Ross King, and Stephen Oliver. Combining inductive logic programming, active learning and robotics to discover the function of genes. Electronic Transactions in Artificial Intelligence, 5(B), 2001. URL: http://www.ep.liu.se/ej/etai/2001/001/.
  5. CFS scheduler. https://www.kernel.org/doc/Documentation/scheduler/sched-design-CFS.txt. Accessed: 2025-01-15.
  6. Jian Chen and Lizy John. Energy-aware application scheduling on a heterogeneous multi-core system. In IEEE International Symposium on Workload Characterization, pages 5-13, 2008. Google Scholar
  7. Andrew Cropper and Sebastijan Dumančić. Inductive logic programming at 30: A new introduction. Journal of Artificial Intelligence Research, 74:765-850, 2022. URL: https://doi.org/10.1613/JAIR.1.13507.
  8. Andrew Cropper and Rolf Morel. Learning programs by learning from failures. Machine Learning, 110(4):801-856, 2021. URL: https://doi.org/10.1007/S10994-020-05934-Z.
  9. Eduardo Cruz, Laércio Lima Pilla, and Philippe Navaux. An efficient algorithm for communication-based task mapping. In Euromicro International Conference on Parallel, Distributed, and Network-Based Processing. IEEE, 2015. Google Scholar
  10. Brahmaiah Gandham and Praveen Alapati. OCC pinning: Optimizing concurrent computations through thread pinning. In Workshop on Advanced Tools, Programming Languages, and PLatforms for Implementing and Evaluating algorithms for Distributed Systems, pages 1-5, 2024. URL: https://doi.org/10.1145/3663338.3665829.
  11. Nathan Hanford, Vishal Ahuja, Matthew Farrens, Dipak Ghosal, Mehmet Balman, Eric Pouyoul, and Brian Tierney. Improving network performance on multicore systems: Impact of core affinities on high throughput flows. Future Generation Computer Systems, 56:277-283, 2016. URL: https://doi.org/10.1016/J.FUTURE.2015.09.012.
  12. Nen-Fu Huang and Wen-Yen Tsai. qcAffin: A hardware topology aware interrupt affinitizing and balancing scheme for multi-core and multi-queue packet processing systems. IEEE Transactions on Parallel and Distributed Systems, 27(6), 2016. URL: https://doi.org/10.1109/TPDS.2015.2453960.
  13. Ross King, Jem Rowland, Stephen Oliver, Michael Young, Wayne Aubrey, Emma Byrne, Maria Liakata, Magdalena Markham, Pinar Pir, Larisa Soldatova, Andrew Sparkes, Kenneth Whelan, and Amanda Clare. The automation of science. Science, 324(5923):85-89, 2009. Google Scholar
  14. Ross King, Kenneth Whelan, Ffion Jones, Philip Reiser, Christopher Bryant, Stephen Muggleton, Douglas Kell, and Stephen Oliver. Functional genomic hypothesis generation and experimentation by a robot scientist. Nature, 427(6971):247-252, 2004. Google Scholar
  15. Tobias Klug, Michael Ott, Josef Weidendorfer, and Carsten Trinitis. autopin - automated optimization of thread-to-core pinning on multicore systems. Transactions on High-Performance Embedded Architectures and Compilers, pages 219-235, 2011. URL: https://doi.org/10.1007/978-3-642-19448-1_12.
  16. Zexin Li, Yuqun Zhang, Ao Ding, Husheng Zhou, and Cong Liu. Efficient algorithms for task mapping on heterogeneous CPU/GPU platforms for fast completion time. Journal of Systems Architecture, 114, 2021. URL: https://doi.org/10.1016/J.SYSARC.2020.101936.
  17. Sorin Manolache, Petru Eles, and Zebo Peng. Task mapping and priority assignment for soft real-time applications under deadline miss ratio constraints. ACM Transactions on Embedded Computing Systems, 7(2):1-35, 2008. URL: https://doi.org/10.1145/1331331.1331343.
  18. Miloš Panić, Sebastian Kehr, Eduardo Quiñones, Bert Boddecker, Jaume Abella, and Francisco Cazorla. RunPar: An allocation algorithm for automotive applications exploiting runnable parallelism in multicores. In International Conference on Hardware/Software Codesign and System Synthesis, pages 1-10. Association for Computing Machinery, 2014. Google Scholar
  19. Don Pannell. Choosing the right TSN tools to meet a bounded latency. Technical report, NXP, 2021. Google Scholar
  20. Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Pearson, 3 edition, 2009. Google Scholar
  21. James Salehi, James Kurose, and Don Towsley. The effectiveness of affinity-based scheduling in multiprocessor network protocol processing (extended version). IEEE/ACM Transactions on Networking, 4(4):516-530, 1996. URL: https://doi.org/10.1109/90.532862.
  22. Xi Tao, Weipeng Cao, Yinghui Pan, Ye Liu, and Zhong Ming. Improving QoS of workloads with CPU pinning: A deep reinforcement learning approach. In IEEE/ACM International Symposium on Quality of Service, pages 1-2, 2024. URL: https://doi.org/10.1109/IWQOS61813.2024.10682839.
  23. Alaa Tharwat and Wolfram Schenck. A survey on active learning: State-of-the-art, practical challenges and research directions. Mathematics, 11(4):820, 2023. Google Scholar
Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2025 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact