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Improved Elastic Scheduling Algorithms for Implicit-Deadline Tasks

Authors Marion Sudvarg , Christopher Gill , Sanjoy Baruah

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LITES.10.2.2.pdf
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Subject Classification

ACM Subject Classification
  • Computer systems organization → Real-time systems
Keywords
  • real-time systems
  • elastic scheduling
  • scheduling algorithms

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Abstract

Elastic scheduling provides a framework under which the utilizations of recurrent tasks are reduced by increasing their periods in response to system overload. The original elastic scheduling model was proposed by Buttazzo et al. in 1998 for implicit-deadline tasks on a uniprocessor and decreases task utilizations to satisfy a schedulable utilization bound. In 2019, Orr and Baruah extended the framework to multiprocessor scheduling of implicit-deadline tasks. In this paper, we propose, analyze, and evaluate new elastic scheduling algorithms for several of the scheduling policies considered in these prior works. In particular, (i) we evaluate an algorithm that we proposed as a short note in the Real-Time Systems journal and demonstrate that it allows for faster admission control than the algorithm of Buttazzo et al. when applied to uniprocessor and fluid scheduling. (ii) We also present faster elastic scheduling algorithms for partitioned EDF scheduling. Finally, (iii) we provide polynomial-time exact elastic scheduling algorithms for global EDF and global RM.

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Marion Sudvarg, Christopher Gill, and Sanjoy Baruah. Improved Elastic Scheduling Algorithms for Implicit-Deadline Tasks. In LITES, Volume 10, Issue 2 (2025): Special Issue on Industrial Real-Time Systems. Leibniz Transactions on Embedded Systems, Volume 10, Issue 2, pp. 2:1-2:36, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LITES.10.2.2

Author Details

Marion Sudvarg
  • Washington University in St. Louis, MO, USA
Christopher Gill
  • Washington University in St. Louis, MO, USA
Sanjoy Baruah
  • Washington University in St. Louis, MO, USA

Funding

This research was supported in part by NSF grants CPS-2229290 and CNS-2141256 and a Washington University seed grant.

Acknowledgements

The authors would like to thank the anonymous reviewers for their helpful comments. Thanks also to the TPC Chairs of SIES 2024 (Daniel Casini, Qingxu Deng, and Zhishan Guo) for inviting us to submit our work to this special issue.

References

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