Document Open Access Logo

Enabling Containerisation of Distributed Applications with Real-Time Constraints

Authors Nasim Samimi , Luca Abeni , Daniel Casini , Mauro Marinoni , Twan Basten , Mitra Nasri , Marc Geilen , Alessandro Biondi

PDF

File

Thumbnail PDF
PDF
LIPIcs.ECRTS.2025.3.pdf
  • Filesize: 1.18 MB
  • 29 pages

Document Identifiers

Subject Classification

ACM Subject Classification
  • Computer systems organization → Real-time systems
  • Software and its engineering
Keywords
  • Kubernetes
  • real-time containers
  • SCHED_DEADLINE
  • KubeRay

Metrics

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

Abstract

Containerisation is becoming a cornerstone of modern distributed systems, thanks to their lightweight virtualisation, high portability, and seamless integration with orchestration tools such as Kubernetes. The usage of containers has also gained traction in real-time cyber-physical systems, such as software-defined vehicles, which are characterised by strict timing requirements to ensure safety and performance. Nevertheless, ensuring real-time execution of co-located containers is challenging because of mutual interference due to the sharing of the same processing hardware. Existing parallel computing frameworks such as Ray and its Kubernetes-enabled variant, KubeRay, excel in distributed computation but lack support for scheduling policies that allow guaranteeing real-time timing constraints and CPU resource isolation between containers, such as the SCHED_DEADLINE policy of Linux. To fill this gap, this paper extends Ray to support real-time containers that leverage SCHED_DEADLINE. To this end, we propose KubeDeadline, a novel, modular Kubernetes extension to support SCHED_DEADLINE. We evaluate our approach through extensive experiments, using synthetic workloads and a case study based on the MobileNet and EfficientNet deep neural networks. Our evaluation shows that KubeDeadline ensures deadline compliance in all synthetic workloads, adds minimal deployment overhead (in the order of milliseconds), and achieves lower worst-case response times, up to 4 times lower, than vanilla Kubernetes under background interference.

Cite As Get BibTex

Nasim Samimi, Luca Abeni, Daniel Casini, Mauro Marinoni, Twan Basten, Mitra Nasri, Marc Geilen, and Alessandro Biondi. Enabling Containerisation of Distributed Applications with Real-Time Constraints. In 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 335, pp. 3:1-3:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.ECRTS.2025.3

Author Details

Nasim Samimi
  • Eindhoven University of Technology, The Netherlands
Luca Abeni
  • Scuola Superiore Sant'Anna, Pisa, Italy
Daniel Casini
  • Scuola Superiore Sant'Anna, Pisa, Italy
Mauro Marinoni
  • Scuola Superiore Sant'Anna, Pisa, Italy
Twan Basten
  • Eindhoven University of Technology, The Netherlands
Mitra Nasri
  • Eindhoven University of Technology, The Netherlands
Marc Geilen
  • Eindhoven University of Technology, The Netherlands
Alessandro Biondi
  • Scuola Superiore Sant'Anna, Pisa, Italy

Funding

This work was partially supported by the EU ECSEL project TRANSACT grant agreement No. 101007260 and the EU Horizon Europe Framework Programme project NANCY grant agreement No. 101096456.

Supplementary Materials

References

  1. Build production-grade data and ml workflows, hassle-free with flyte. Accessed: 2025-04-24. URL: https://flyte.org/.
  2. CFS scheduler — the Linux kernel documentation. Accessed: 2025-04-28. URL: https://docs.kernel.org/scheduler/sched-design-CFS.html.
  3. Kubernetes documentation. https://kubernetes.io/. Accessed: 2024-11-14.
  4. Kubernetes scheduler | Kubernetes. Accessed: 2025-04-28. URL: https://kubernetes.io/docs/concepts/scheduling-eviction/kube-scheduler/.
  5. Kubernetes scheduling framework. https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework. Accessed: 2024-11-14.
  6. Nvidia k8s-dra-driver. https://github.com/NVIDIA/k8s-dra-driver. Accessed: 2024-11-14.
  7. Pod quality of service classes | Kubernetes. Accessed: 2025-04-28. URL: https://kubernetes.io/docs/concepts/workloads/pods/pod-qos/.
  8. Real-time group scheduling — the Linux kernel documentation. Accessed: 2025-04-28. URL: https://docs.kernel.org/scheduler/sched-rt-group.html.
  9. Scale machine learning & AI computing | Ray by anyscale. Accessed: 2025-04-29. URL: https://www.ray.io/.
  10. Network Functions Virtualisation – Introductory White Paper – An Introduction, Benefits, Enablers, Challenges & Call for Action, 2012. SDN and OpenFlow World Congress. URL: https://portal.etsi.org/NFV/NFV_White_Paper.pdf.
  11. L. Abeni and G. Buttazzo. Integrating multimedia applications in hard real-time systems. In Proceedings of the 19th IEEE Real-Time Systems Symposium (RTSS 1998), Madrid, Spain, december 2-4 1998. URL: https://doi.org/10.1109/REAL.1998.739726.
  12. Luca Abeni. Virtualized real-time workloads in containers and virtual machines. Journal of Systems Architecture, 154:103238, 2024. URL: https://doi.org/10.1016/J.SYSARC.2024.103238.
  13. Luca Abeni, Alessio Balsini, and Tommaso Cucinotta. Container-based real-time scheduling in the Linux kernel. ACM SIGBED Review, 16:33-38, November 2019. URL: https://doi.org/10.1145/3373400.3373405.
  14. Luca Abeni, Alessandro Biondi, and Enrico Bini. Partitioning real-time workloads on multi-core virtual machines. Journal of Systems Architecture, 131:102733, 2022. URL: https://doi.org/10.1016/j.sysarc.2022.102733.
  15. Luca Abeni, Tommaso Cucinotta, and Daniel Casini. Period estimation for Linux-based edge computing virtualization with strong temporal isolation. In 2024 IEEE 3rd Real-Time and Intelligent Edge Computing Workshop (RAGE), pages 1-6, 2024. URL: https://doi.org/10.1109/RAGE62451.2024.00013.
  16. Luca Abeni, Giuseppe Lipari, and Juri Lelli. Constant bandwidth server revisited. ACM SIGBED Review, 1291, January 2015. URL: https://doi.org/10.1145/2724942.2724945.
  17. Jason Anderson, Hongxin Hu, Udit Agarwal, Craig Lowery, Hongda Li, and Amy Apon. Performance considerations of network functions virtualization using containers. In 2016 International Conference on Computing, Networking and Communications (ICNC), pages 1-7, 2016. URL: https://doi.org/10.1109/ICCNC.2016.7440668.
  18. Marco Barletta, Marcello Cinque, Luigi De Simone, and Raffaele Della Corte. Criticality-aware monitoring and orchestration for containerized industry 4.0 environments. ACM Transactions on Embedded Computing Systems, 23(1):1-28, 2024. URL: https://doi.org/10.1145/3604567.
  19. Marco Barletta, Marcello Cinque, Luigi De Simone, Raffaele Della Corte, Giorgio Farina, and Daniele Ottaviano. Partitioned containers: Towards safe clouds for industrial applications. In 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S), pages 84-88, 2023. URL: https://doi.org/10.1109/DSN-S58398.2023.00029.
  20. Enrico Bini, Marco Bertogna, and Sanjoy Baruah. Virtual multiprocessor platforms: Specification and use. Proceedings - Real-Time Systems Symposium, pages 437-446, 2009. URL: https://doi.org/10.1109/RTSS.2009.35.
  21. Steven Blake, David Black, Mark Carlson, Elwyn Davies, Zheng Wang, and Walter Weiss. An architecture for differentiated services. Technical report, The Internet Society, 1998. URL: https://doi.org/10.17487/RFC2475.
  22. Brian E Carpenter and Kathleen Nichols. Differentiated services in the internet. Proceedings of the IEEE, 90(9):1479-1494, 2002. URL: https://doi.org/10.1109/JPROC.2002.802000.
  23. Fabio Checconi, Tommaso Cucinotta, Dario Faggioli, Giuseppe Lipari, et al. Hierarchical multiprocessor CPU reservations for the Linux kernel. In Proceedings of the 5th international workshop on operating systems platforms for embedded real-time applications (OSPERT 2009), Dublin, Ireland, pages 15-22, 2009. URL: http://www.artist-embedded.org/docs/Events/2009/OSPERT/Proceedings-PreWorkshop.pdf#page=15.
  24. T. Cucinotta, F. Checconi, Z. Zlatev, J. Papay, M. Boniface, G. Kousiouris, D. Kyriazis, T. Varvarigou, S. Berger, D. Lamp, A. Mazzetti, T. Voith, and M. Stein. Virtualised e-learning with real-time guarantees on the irmos platform. In 2010 IEEE International Conference on Service-Oriented Computing and Applications (SOCA), pages 1-8, 2010. URL: https://doi.org/10.1109/SOCA.2010.5707166.
  25. Tommaso Cucinotta, Luca Abeni, Mauro Marinoni, Alessio Balsini, and Carlo Vitucci. Reducing temporal interference in private clouds through real-time containers. In 2019 IEEE International Conference on Edge Computing (EDGE), pages 124-131, 2019. URL: https://doi.org/10.1109/EDGE.2019.00036.
  26. Tommaso Cucinotta, Luca Abeni, Mauro Marinoni, Riccardo Mancini, and Carlo Vitucci. Strong temporal isolation among containers in openstack for NFV services. IEEE Transactions on Cloud Computing, 2021. URL: https://doi.org/10.1109/TCC.2021.3116183.
  27. Tommaso Cucinotta, Fabio Checconi, Luca Abeni, and Luigi Palopoli. Adaptive real-time scheduling for legacy multimedia applications. ACM Trans. Embed. Comput. Syst., 11(4), January 2013. URL: https://doi.org/10.1145/2362336.2362353.
  28. Daniel Bristot de Oliveira, Daniel Casini, and Tommaso Cucinotta. Operating system noise in the Linux kernel. IEEE Transactions on Computers, 72(11):196-207, January 2023. URL: https://doi.org/10.1109/TC.2023.3248921.
  29. Arvind Easwaran, Insik Shin, and Insup Lee. Optimal virtual cluster-based multiprocessor scheduling. Real-Time Systems, 43(1):25-59, September 2009. URL: https://doi.org/10.1007/S11241-009-9073-X.
  30. Paul Emberson, Roger Stafford, and Robert I Davis. Techniques for the synthesis of multiprocessor tasksets. In Proceedings 1st International Workshop on Analysis Tools and Methodologies for Embedded and Real-time Systems (WATERS 2010), pages 6-11, 2010. URL: https://retis.sssup.it/waters2010/waters2010.pdf#page=6.
  31. Stefano Fiori, Luca Abeni, and Tommaso Cucinotta. RT-Kubernetes - containerized real-time cloud computing. Proceedings of the ACM Symposium on Applied Computing, pages 36-39, April 2022. URL: https://doi.org/10.1145/3477314.3507216.
  32. Gautam Gala, Carlos Rodriguez, Veaceslav Monaco, Javier Castillo, Gerhard Fohler, Veaceslav Falico, and Sergey Tverdyshev. Monitoring framework to support mixed-criticality applications on multicore platforms. Proceedings - 2022 25th Euromicro Conference on Digital System Design, DSD 2022, pages 649-656, 2022. URL: https://doi.org/10.1109/DSD57027.2022.00092.
  33. Pablo González-Nalda, Ismael Etxeberria-Agiriano, Isidro Calvo, and Mari Carmen Otero. A modular CPS architecture design based on ROS and Docker. International Journal on Interactive Design and Manufacturing (IJIDeM), 11(4):949-955, 2017. URL: https://doi.org/10.1007/s12008-016-0313-8.
  34. Pawan Goyal, Xingang Guo, and Harrick M. Vin. A hierarchical CPU scheduler for multimedia operating systems. In Karin Petersen and Willy Zwaenepoel, editors, Proceedings of the Second USENIX Symposium on Operating Systems Design and Implementation (OSDI), Seattle, Washington, USA, October 28-31, 1996, pages 107-121. ACM, 1996. URL: https://doi.org/10.1145/238721.238766.
  35. Arne Hamann, Selma Saidi, David Ginthoer, Christian Wietfeld, and Dirk Ziegenbein. Building end-to-end IoT applications with QoS guarantees. In 2020 57th ACM/IEEE Design Automation Conference (DAC), pages 1-6, 2020. URL: https://doi.org/10.1109/DAC18072.2020.9218564.
  36. Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. CoRR, April 2017. URL: https://arxiv.org/abs/1704.04861.
  37. Isser Kadusale, Gautam Gala, and Gerhard Fohler. WASM and containers for real-time serverless edge computing. JRWRTC 2024, page 11, 2024. URL: https://cister-labs.pt/rtns24/JRWRTC2024-proceedings.pdf#page=15.
  38. Ali Kanso, Edi Palencia, Kinshuman Patra, Jiaxin Shan, Mengyuan Chao, Xu Wei, Tengwei Cai, Kang Chen, and Shuai Qiao. Designing a Kubernetes operator for machine learning applications. Proceedings of the Seventh International Workshop on Container Technologies and Container Clouds, 2021. URL: https://doi.org/10.1145/3493649.3493654.
  39. D. Kreutz, F. M. V. Ramos, P. E. Veríssimo, C. E. Rothenberg, S. Azodolmolky, and S. Uhlig. Software-defined networking: A comprehensive survey. Proceedings of the IEEE, 103(1):14-76, January 2015. URL: https://doi.org/10.1109/JPROC.2014.2371999.
  40. Jaewoo Lee, Sisu Xi, Sanjian Chen, Linh T.X. Phan, Chris Gill, Insup Lee, Chenyang Lu, and Oleg Sokolsky. Realizing compositional scheduling through virtualization. In 2012 IEEE 18th Real Time and Embedded Technology and Applications Symposium, pages 13-22, 2012. URL: https://doi.org/10.1109/RTAS.2012.20.
  41. J. Lelli, C. Scordino, L. Abeni, and D. Faggioli. Deadline scheduling in the Linux kernel. Software: Practice and Experience, 46(6):821-839, 2016. URL: https://doi.org/10.1002/SPE.2335.
  42. Hennadiy Leontyev and James H. Anderson. A hierarchical multiprocessor bandwidth reservation scheme with timing guarantees. Real-Time Systems, 43(1):60-92, 2009. URL: https://doi.org/10.1007/S11241-009-9076-7.
  43. Giuseppe Lipari and Sanjoy Baruah. A hierarchical extension to the constant bandwidth server framework. In Proceedings Seventh IEEE Real-Time Technology and Applications Symposium, pages 26-35. IEEE, 2001. URL: https://doi.org/10.1109/RTTAS.2001.929863.
  44. C. L. Liu and James W. Layland. Scheduling algorithms for multiprogramming in a hard-real-time environment. Journal of the ACM (JACM), 20(1):46-61, 1973. URL: https://doi.org/10.1145/321738.321743.
  45. Francesco Lumpp, Franco Fummi, Hiren D Patel, and Nicola Bombieri. Containerization and orchestration of software for autonomous mobile robots: A case study of mixed-criticality tasks across edge-cloud computing platforms. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 9708-9713. IEEE, 2022. URL: https://doi.org/10.1109/IROS47612.2022.9981581.
  46. Francesco Lumpp, Franco Fummi, Hiren D Patel, and Nicola Bombieri. Enabling Kubernetes orchestration of mixed-criticality software for autonomous mobile robots. IEEE Transactions on Robotics, 40:540-553, 2023. URL: https://doi.org/10.1109/TRO.2023.3334642.
  47. Gabriele Monaco, Gautam Gala, and Gerhard Fohler. Shared resource orchestration extensions for Kubernetes to support real-time cloud containers. Proceedings - 2023 IEEE 26th International Symposium on Real-Time Distributed Computing, ISORC 2023, pages 97-106, 2023. URL: https://doi.org/10.1109/ISORC58943.2023.00022.
  48. Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I Jordan, et al. Ray: A distributed framework for emerging AI applications. In 13th USENIX symposium on operating systems design and implementation (OSDI 18), pages 561-577, 2018. URL: https://www.usenix.org/conference/osdi18/presentation/nishihara.
  49. Philipp Mundhenk, Arne Hamann, Andreas Heyl, and Dirk Ziegenbein. Reliable distributed systems. In Design, Automation & Test in Europe Conference & Exhibition (DATE), pages 287-291. IEEE, 2022. URL: https://doi.org/10.23919/DATE54114.2022.9774734.
  50. Linh T. X. Phan, Jaewoo Lee, Arvind Easwaran, Vinay Ramaswamy, Sanjian Chen, Insup Lee, and Oleg Sokolsky. CARTS: A tool for compositional analysis of real-time systems. SIGBED Review, 8(1):62-63, March 2011. URL: https://doi.org/10.1145/1967021.1967029.
  51. Kun Ran, Yingbo Cui, Zihang Wang, and Shaoliang Peng. Performance evaluation of Spark, Ray and MPI: A case study on long read alignment algorithm. In Algorithms and Architectures for Parallel Processing: 23rd International Conference, ICA3PP 2023, Tianjin, China, October 20–22, 2023, Proceedings, Part III, pages 57-76, Berlin, Heidelberg, 2024. Springer-Verlag. URL: https://doi.org/10.1007/978-981-97-0798-0_4.
  52. Rui Reis, Pedro M. Santos, Mario J. Sousa, Nuno Martins, Joana Sousa, and Luis Almeida. LEM: a tool for large-scale workflow control in edge-based industry 5.0 applications. Proceedings - 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023, pages 317-323, 2023. URL: https://doi.org/10.1109/DCOSS-IOT58021.2023.00059.
  53. Insik Shin and Insup Lee. Compositional real-time scheduling framework. Real-Time Systems Symposium, pages 57-67, 2004. URL: https://doi.org/10.1109/REAL.2004.15.
  54. Michael Sollfrank, Frieder Loch, Steef Denteneer, and Birgit Vogel-Heuser. Evaluating Docker for lightweight virtualization of distributed and time-sensitive applications in industrial automation. IEEE Transactions on Industrial Informatics, 17(5):3566-3576, 2021. URL: https://doi.org/10.1109/TII.2020.3022843.
  55. I. Stoica, H. Abdel-Wahab, K. Jeffay, S.K. Baruah, J.E. Gehrke, and C.G. Plaxton. A proportional share resource allocation algorithm for real-time, time-shared systems. In 17th IEEE Real-Time Systems Symposium, pages 288-299, 1996. URL: https://doi.org/10.1109/REAL.1996.563725.
  56. Václav Struhár, Silviu S. Craciunas, Mohammad Ashjaei, Moris Behnam, and Alessandro V. Papadopoulos. Hierarchical resource orchestration framework for real-time containers. ACM Trans. Embed. Comput. Syst., April 2023. URL: https://doi.org/10.1145/3592856.
  57. Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105-6114. PMLR, 2019. URL: http://proceedings.mlr.press/v97/tan19a.html.
  58. The New Stack. How Ray, a distributed AI framework, helps power ChatGPT, 2023. Accessed: 2025-02-17. URL: https://thenewstack.io/how-ray-a-distributed-ai-framework-helps-power-chatgpt.
  59. Uber Technologies Inc. How Uber uses Ray to optimize the rides business, 2023. Accessed: 2025-02-17. URL: https://www.uber.com/en-IT/blog/how-uber-uses-ray-to-optimize-the-rides-business/.
  60. Stefan Walser, Jan Ruh, and Silviu S Craciunas. Real-time container orchestration based on time-utility functions. In IEEE 20th International Conference on Factory Communication Systems (WFCS), pages 1-8. IEEE, 2024. URL: https://doi.org/10.1109/WFCS60972.2024.10541041.
  61. Wen Wu, Kaige Qu, Peng Yang, Ning Zhang, Xuemin Sherman Shen, and Weihua Zhuang. Cost-effective two-stage network slicing for edge-cloud orchestrated vehicular networks. 2022 IEEE/CIC International Conference on Communications in China, ICCC 2022, pages 968-973, 2022. URL: https://doi.org/10.1109/ICCC55456.2022.9880642.
  62. Sisu Xi, Chong Li, Chenyang Lu, Christopher D. Gill, Meng Xu, Linh T.X. Phan, Insup Lee, and Oleg Sokolsky. RT-Open Stack: CPU resource management for real-time cloud computing. In 2015 IEEE 8th International Conference on Cloud Computing, pages 179-186, 2015. URL: https://doi.org/10.1109/CLOUD.2015.33.
  63. Matei Zaharia, Mosharaf Chowdhury, Michael J Franklin, Scott Shenker, and Ion Stoica. Spark: Cluster computing with working sets. In 2nd USENIX workshop on hot topics in cloud computing (HotCloud 10), 2010. URL: https://www.usenix.org/conference/hotcloud-10/spark-cluster-computing-working-sets.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

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