Minimizing a real-time task set through Task Clustering

Antoine Bertout 1, 2, 3 Julien Forget 1, 2, 3 Richard Olejnik 1, 2, 3
1 DART - Contributions of the Data parallelism to real time
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
2 LIFL - DART/Émeraude
LIFL - Laboratoire d'Informatique Fondamentale de Lille
Abstract : In the industry, real-time systems are specified as a set of hundreds of functionalities with timing constraints. Implementing those functionalities as threads in a one-to-one relation is not realistic due to the overhead caused by the large number of threads. In this paper, we present task clustering, which aims at minimizing the number of threads while preserving the schedulability. We prove that our clustering problem is NP-Hard and describe a heuristic to tackle it. Our approach has been applied to fixed-task or fixed-job priority based scheduling policies as Deadline Monotonic (DM) or Earliest Deadline First (EDF).
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Antoine Bertout, Julien Forget, Richard Olejnik. Minimizing a real-time task set through Task Clustering. Proceedings of the 22nd International Conference on Real-Time Networks and Systems, Oct 2014, Versailles, France. pp.23-31, ⟨10.1145/2659787.2659820⟩. ⟨hal-01073565⟩

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