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Real-Time Systems

, Volume 55, Issue 2, pp 387–432 | Cite as

Schedulability analysis of DAG tasks with arbitrary deadlines under global fixed-priority scheduling

  • José FonsecaEmail author
  • Geoffrey Nelissen
  • Vincent Nélis
Article
  • 71 Downloads

Abstract

One of the major sources of pessimism in the response time analysis (RTA) of globally scheduled real-time tasks is the computation of an upper-bound on the inter-task interference. This problem is further exacerbated when intra-task parallelism is permitted because of the complex internal structure of parallel tasks. This paper considers the global fixed-priority (G-FP) scheduling of sporadic real-time tasks when each task is modeled by a directed acyclic graph (DAG) of concurrent subtasks. We present a RTA based on the concept of problem window, a technique that has been extensively used to study the schedulability of sequential task in multiprocessor systems. The problem window approach of RTA usually categorizes interfering jobs in three different groups: carry-in, carry-out and body jobs. In this paper, we propose two novel techniques to derive less pessimistic upper-bounds on the workload produced by the carry-in and carry-out jobs of the interfering tasks. Those new bounds take into account the precedence constraints between subtasks pertaining to the same DAG. We show that with this new characterization of the carry-in and carry-out workload, the proposed schedulability test offers significant improvements on the schedulability of DAG tasks for randomly generated task sets in comparison to state-of-the-art techniques. In fact, we show that, while the state-of-art analysis does not scale with an increasing number of processors when tasks have constrained deadlines, the results of our analysis are barely impacted by the processor count in both the constrained and the arbitrary deadline case.

Keywords

Parallel tasks DAG scheduling Response time analysis Multiprocessor systems Real-time systems 

Notes

Acknowledgements

This work was partially supported by National Funds through FCT/ MCTES (Portuguese Foundation for Science and Technology) and co-financed by ERDF (European Regional Development Fund) under the PT2020 Partnership, within the CISTER Research Unit (CEC/04234).

References

  1. Andersson B, de Niz D (2012) Analyzing global-edf for multiprocessor scheduling of parallel tasks. In: Principles of distributed systems, lecture notes in computer science, vol 7702, pp 16–30Google Scholar
  2. Baker TP (2003) Multiprocessor edf and deadline monotonic schedulability analysis. In: RTSS’03, pp 120–129Google Scholar
  3. Baruah S (2014) Improved multiprocessor global schedulability analysis of sporadic dag task systems. In: ECRTS’14, pp 97–105Google Scholar
  4. Baruah S, Bonifaci V, Marchetti-Spaccamela A (2015) The global edf scheduling of systems of conditional sporadic dag tasks. In: ECRTS’15Google Scholar
  5. Baruah SK, Bonifaci V, Marchetti-Spaccamela A, Stougie L, Wiese A (2012) A generalized parallel task model for recurrent real-time processes. In: RTSS’12, pp 63–72Google Scholar
  6. Bini E, Buttazzo GC (2005) Measuring the performance of schedulability tests. Real-Time Syst 30(1):129–154CrossRefzbMATHGoogle Scholar
  7. Board OAR (2013) OpenMP application program interface version 4.0 http://www.openmp.org/mp-documents/OpenMP4.0.0.pdf
  8. Bonifaci V, Marchetti-Spaccamela A, Stiller S, Wiese A (2013) Feasibility analysis in the sporadic dag task model. In: ECRTS’13Google Scholar
  9. Chwa HS, Lee J, Phan KM, Easwaran A, Shin I (2013) Global edf schedulability analysis for synchronous parallel tasks on multicore platforms. In: ECRTS’13, pp 25–34Google Scholar
  10. Fonseca J, Nélis V, Raravi G, Pinho LM (2015) A multi-dag model for real-time parallel applications with conditional execution. In: SAC’15Google Scholar
  11. Fonseca J, Nelissen G, Nélis V (2017) Improved response time analysis of sporadic dag tasks for global fp scheduling. In: Proceedings of the 25th international conference on real-time networks and systems, pp 28–37. ACMGoogle Scholar
  12. Fonseca J, Nelissen G, Nelis V, Pinho LM (2016) Response time analysis of sporadic dag tasks under partitioned scheduling. In: SIES’16Google Scholar
  13. González-Escribano A, Van Gemund AJC, Cardeñoso Payo V (2002) Mapping unstructured applications into nested parallelism. In: VECPAR’02, pp. 407–420Google Scholar
  14. Guan N, Stigge M, Yi W, Yu G (2009) New response time bounds for fixed priority multiprocessor scheduling. In: 30th IEEE real-time systems symposium, pp 387–397Google Scholar
  15. He X, Yesha Y (1987) Parallel recognition and decomposition of two terminal series parallel graphs. Inf. Comput. 75(1):15–38MathSciNetCrossRefzbMATHGoogle Scholar
  16. Jiang X, Guan N, Long X, Yi W (2017) Semi-federated scheduling of parallel real-time tasks on multiprocessors. In: RTSS’17Google Scholar
  17. Lakshmanan, K., Kato, S., Rajkumar, R (2010) Scheduling parallel real-time tasks on multi-core processors. In: RTSS’10, pp 259–268Google Scholar
  18. Li J, Agrawal K, Lu C, Gill CD (2013) Analysis of global EDF for parallel tasks. In: ECRTS’13, pp 3–13Google Scholar
  19. Li J, Chen J, Agrawal K, Lu C, Gill CD (2014) Analysis of federated and global scheduling for parallel real-time tasks. In: ECRTS’14, pp. 85–96Google Scholar
  20. Maia C, Bertogna M, Nogueira L, Pinho LM (2014) Response-time analysis of synchronous parallel tasks in multiprocessor systems. In: RTNS’14, pp 3–12Google Scholar
  21. Melani A, Bertogna M, Bonifaci V, Marchetti-Spaccamela A, Buttazzo GC (2015) Response-time analysis of conditional dag tasks in multiprocessor systems. In: ECRTS’15, pp 211–221Google Scholar
  22. Melani A, Bertogna M, Bonifaci V, Marchetti-Spaccamela A, Buttazzo GC (2017) Response-time analysis of conditional dag tasks in multiprocessor systems. IEEE Trans Comput 66(2):339–353MathSciNetzbMATHGoogle Scholar
  23. Nelissen G, Berten V, Goossens J, Milojevic D (2012) Techniques optimizing the number of processors to schedule multi-threaded tasks. In: ECRTS, pp 321–330Google Scholar
  24. Parri A, Biondi A, Marinoni M (2015) Response time analysis for g-edf and g-dm scheduling of sporadic dag-tasks with arbitrary deadline. In: RTNS’15, pp 205–214Google Scholar
  25. Pathan R, Voudouris P, Stenstrm P (2018) Scheduling parallel real-time recurrent tasks on multicore platforms. IEEE Trans Parallel Distrib Syst 29(4):915–928CrossRefGoogle Scholar
  26. Qamhieh M, Fauberteau F, George L, Midonnet S (2013) Global edf scheduling of directed acyclic graphs on multiprocessor systems. In: RTNS, pp 287–296Google Scholar
  27. Saifullah A, Agrawal K, Lu C, Gill C (2011) Multi-core real-time scheduling for generalized parallel task models. In: RTSS’11, pp 217–226Google Scholar
  28. Saifullah A, Ferry D, Li J, Agrawal K, Lu C, Gill C (2014) Parallel real-time scheduling of dags. IEEE Trans Parallel Distrib Syst 25(12):3242–3252CrossRefGoogle Scholar
  29. Saifullah A, Li J, Agrawal K, Lu C, Gill C (2013) Multi-core real-time scheduling for generalized parallel task models. Real-Time Syst 49(4):404–435CrossRefzbMATHGoogle Scholar
  30. Valdes J, Tarjan RE, Lawler EL (1979) The recognition of series parallel digraphs. In: STOC’79, pp 1–12Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.CISTER/INESC-TEC, Instituto Superior de Engenharia do PortoPortoPortugal

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