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

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Abstract

In general computing systems, a job (process/task) may suspend 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 observation, 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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Keywords

Multiprocessors, Interactive computer systems, Algorithms, Computer systems

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This paper has been partially supported by DFG, as part of the Collaborative Research Center SFB876 (http://sfb876.tu-dortmund.de/), subproject B2. This work has been partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology) within the CISTER Research Unit (CEC/04234); also by FCT and the EU ECSEL JU under the H2020 Framework Programme, within projects EMC2 (ARTEMIS/0001/2013, JU Grant No. 621429) and CONCERTO (ARTEMIS/0003/2012, JU GrantNo. 333053). Thiswork has been partially supported by NSFC (Grant No. 61802052), and by the China Postdoctoral Science Foundation Funded Project (Grant No. 2017M612947). This material is based upon work funded and supported by NSF grants OISE 1427824, CNS 1527727, and CNS CAREER 1750263, and the Department of Defense under Contract No. FA8721-05-C-0003 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center.

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CC BY 4.0 (Attribution), ©2018 The Authors

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