2 Search Results for "Côrte-Real, Joana"

Document

DOI: 10.4230/LIPIcs.ECRTS.2025.3

Enabling Containerisation of Distributed Applications with Real-Time Constraints

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

Published in: LIPIcs, Volume 335, 37th Euromicro Conference on Real-Time Systems (ECRTS 2025)

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

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)

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@InProceedings{samimi_et_al:LIPIcs.ECRTS.2025.3,
  author =	{Samimi, Nasim and Abeni, Luca and Casini, Daniel and Marinoni, Mauro and Basten, Twan and Nasri, Mitra and Geilen, Marc and Biondi, Alessandro},
  title =	{{Enabling Containerisation of Distributed Applications with Real-Time Constraints}},
  booktitle =	{37th Euromicro Conference on Real-Time Systems (ECRTS 2025)},
  pages =	{3:1--3:29},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-377-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{335},
  editor =	{Mancuso, Renato},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2025.3},
  URN =		{urn:nbn:de:0030-drops-235816},
  doi =		{10.4230/LIPIcs.ECRTS.2025.3},
  annote =	{Keywords: Kubernetes, real-time containers, SCHED\underlineDEADLINE, KubeRay}
}

Document

DOI: 10.4230/OASIcs.ICLP.2018.6

Improving Candidate Quality of Probabilistic Logic Models

Authors: Joana Côrte-Real, Anton Dries, Inês Dutra, and Ricardo Rocha

Published in: OASIcs, Volume 64, Technical Communications of the 34th International Conference on Logic Programming (ICLP 2018)

Abstract

Many real-world phenomena exhibit both relational structure and uncertainty. Probabilistic Inductive Logic Programming (PILP) uses Inductive Logic Programming (ILP) extended with probabilistic facts to produce meaningful and interpretable models for real-world phenomena. This merge between First Order Logic (FOL) theories and uncertainty makes PILP a very adequate tool for knowledge representation and extraction. However, this flexibility is coupled with a problem (inherited from ILP) of exponential search space growth and so, often, only a subset of all possible models is explored due to limited resources. Furthermore, the probabilistic evaluation of FOL theories, coming from the underlying probabilistic logic language and its solver, is also computationally demanding. This work introduces a prediction-based pruning strategy, which can reduce the search space based on the probabilistic evaluation of models, and a safe pruning criterion, which guarantees that the optimal model is not pruned away, as well as two alternative more aggressive criteria that do not provide this guarantee. Experiments performed using three benchmarks from different areas show that prediction pruning is effective in (i) maintaining predictive accuracy for all criteria and experimental settings; (ii) reducing the execution time when using some of the more aggressive criteria, compared to using no pruning; and (iii) selecting better candidate models in limited resource settings, also when compared to using no pruning.

Cite as

Joana Côrte-Real, Anton Dries, Inês Dutra, and Ricardo Rocha. Improving Candidate Quality of Probabilistic Logic Models. In Technical Communications of the 34th International Conference on Logic Programming (ICLP 2018). Open Access Series in Informatics (OASIcs), Volume 64, pp. 6:1-6:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{cortereal_et_al:OASIcs.ICLP.2018.6,
  author =	{C\^{o}rte-Real, Joana and Dries, Anton and Dutra, In\^{e}s and Rocha, Ricardo},
  title =	{{Improving Candidate Quality of Probabilistic Logic Models}},
  booktitle =	{Technical Communications of the 34th International Conference on Logic Programming (ICLP 2018)},
  pages =	{6:1--6:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-090-3},
  ISSN =	{2190-6807},
  year =	{2018},
  volume =	{64},
  editor =	{Dal Palu', Alessandro and Tarau, Paul and Saeedloei, Neda and Fodor, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICLP.2018.6},
  URN =		{urn:nbn:de:0030-drops-98722},
  doi =		{10.4230/OASIcs.ICLP.2018.6},
  annote =	{Keywords: Relational Machine Learning, Probabilistic Inductive Logic Programming, Search Space Pruning, Model Quality, Experiments}
}

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2 Search Results for "Côrte-Real, Joana"

Enabling Containerisation of Distributed Applications with Real-Time Constraints

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Improving Candidate Quality of Probabilistic Logic Models

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