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Tighter Worst-Case Response Time Bounds for Jitter-Based Self-Suspension Analysis

Authors Mario Günzel , Georg von der Brüggen , Jian-Jia Chen

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ACM Subject Classification
  • Computer systems organization → Real-time systems
  • Computer systems organization → Embedded systems
  • Software and its engineering → Real-time schedulability
Keywords
  • Worst-Case Response Time
  • WCRT
  • Jitter
  • Self-Suspension
  • Analysis

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Abstract

Tasks are called self-suspending if they can yield their ready state (specifically, releasing the processor while having highest priority) despite being incomplete, for instance, to offload computation to an external device or when waiting on access rights for shared resources or data. This self-suspending behavior requires special treatment when applying analytical results to compute worst-case response time bounds. One typical treatment is modeling self-suspension as release jitter in a so-called jitter-based analysis. The state of the art, when considering task-level fixed-priority scheduling, individually quantifies the jitter term of each higher-priority task by its worst-case response time minus its worst-case execution time.
This work tightens the jitter term by taking the execution behavior of the other higher-priority tasks into account. Our improved jitter-based analysis analytically dominates the previous jitter-based analysis. Moreover, an evaluation for synthetically generated sporadic tasks demonstrates that this jitter term results in tighter worst-case response time bounds for self-suspending tasks. We observe an improvement for up to 55.89 % of the tasksets compared to the previous jitter-based analysis.

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Mario Günzel, Georg von der Brüggen, and Jian-Jia Chen. Tighter Worst-Case Response Time Bounds for Jitter-Based Self-Suspension Analysis. In 36th Euromicro Conference on Real-Time Systems (ECRTS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 298, pp. 4:1-4:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.ECRTS.2024.4

Author Details

Mario Günzel
  • TU Dortmund University, Germany
Georg von der Brüggen
  • TU Dortmund University, Germany
Jian-Jia Chen
  • TU Dortmund University, Germany

Funding

This work has been supported by Deutsche Forschungsgemeinschaft (DFG), as part of Sus-Aware (Project No. 398602212). This result is part of a project (PropRT) that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 865170).

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