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Many Suspensions, Many Problems: A Review of Self-Suspending Tasks in Real-Time Systems

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Author(s)

  • Jian-Jia Chen
  • Geoffrey Nelissen
  • Wen-Hung Kevin Huang
  • Maolin Yang
  • Bjorn Brandenburg
  • Konstantinos Bletsas
  • Cong Liu
  • Pascal Richard
  • Frederic Ridouard
  • Neil Cameron Audsley
  • Raj Rajkumar
  • Dionisio de Niz
  • Georg von der Bruggen

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Publication details

JournalReal-Time Systems
DateAccepted/In press - 13 Aug 2018
DateE-pub ahead of print (current) - 11 Sep 2018
Number of pages59
Early online date11/09/18
Original languageEnglish

Abstract

In general computing systems, a job (process/task) may sus- pend itself whilst it is waiting for some activity to complete, e.g., an accelerator to return data. In real-time systems, such self-suspension can cause substantial performance/schedulability degradation. This observa- tion, first made in 1988, has led to the investigation of the impact of self-suspension on timing predictability, and many relevant results have been published since. Unfortunately, as it has recently come to light, a number of the existing results are flawed.
To provide a correct platform on which future research can be built, this paper reviews the state of the art in the design and analysis of scheduling algorithms and schedulability tests for self-suspending tasks in real-time systems. We provide (1) a systematic description of how self-suspending tasks can be handled in both soft and hard real-time systems; (2) an explanation of the existing misconceptions and their potential remedies; (3) an assessment of the influence of such flawed analyses on partitioned multiprocessor fixed-priority scheduling when tasks synchronize access to shared resources; and (4) a discussion of the computational complexity of analyses for different self-suspension task models.

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© The Author(s) 2018

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