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A Measurement-Based Model for Parallel Real-Time Tasks

Authors Kunal Agrawal , Sanjoy Baruah



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Author Details

Kunal Agrawal
  • Washington University in St. Louis, St. Louis, MO, USA
Sanjoy Baruah
  • Washington University in St. Louis, St. Louis, MO, USA

Cite AsGet BibTex

Kunal Agrawal and Sanjoy Baruah. A Measurement-Based Model for Parallel Real-Time Tasks. In 30th Euromicro Conference on Real-Time Systems (ECRTS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 106, pp. 5:1-5:19, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.ECRTS.2018.5

Abstract

Under the federated paradigm of multiprocessor scheduling, a set of processors is reserved for the exclusive use of each real-time task. If tasks are characterized very conservatively (as is typical in safety-critical systems), it is likely that most invocations of the task will have computational demand far below the worst-case characterization, and could have been scheduled correctly upon far fewer processors than were assigned to it assuming the worst-case characterization of its run-time behavior. Provided we could safely determine during run-time when all the processors are going to be needed, for the rest of the time the unneeded processors could be idled in low-energy "sleep" mode, or used for executing non-real time work in the background. In this paper we propose a model for representing parallelizable real-time tasks in a manner that permits us to do so. Our model does not require us to have fine-grained knowledge of the internal structure of the code represented by the task; rather, it characterizes each task by a few parameters that are obtained by repeatedly executing the code under different conditions and measuring the run-times.

Subject Classification

ACM Subject Classification
  • Computer systems organization → Real-time systems
  • Software and its engineering → Real-time schedulability
  • Theory of computation → Parallel computing models
Keywords
  • multiprocessor federated scheduling
  • parallel tasks
  • work and span
  • mixed criticality

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