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Probabilistic Schedulability Analysis for Mixed-Criticality DAG Tasks on Multiprocessors

Authors Hiroto Takahashi , Atsushi Yano , Takuya Azumi

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LIPIcs.ECRTS.2026.3.pdf
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Subject Classification

ACM Subject Classification
  • Computer systems organization → Real-time systems
Keywords
  • Mixed-Criticality Systems
  • DAG Tasks
  • Probabilistic Analysis
  • Federated Scheduling
  • Real-Time Systems

Metrics

Abstract

Mixed-criticality DAG task systems on multiprocessors require schedulability analysis to guarantee that safety-critical tasks meet their deadlines. Conventional approaches rely on worst-case execution times (WCETs), which account for extremely rare pathological scenarios and consequently lead to significant over-provisioning of processor cores. This paper proposes a probabilistic schedulability analysis that exploits the statistical rarity of multiple vertices within a DAG exceeding their expected execution budgets. By grouping vertices within each DAG into small clusters and bounding the probability that more than one vertex in a cluster overruns, the method assigns each cluster a tighter execution budget than the sum of individual WCETs, thereby reducing the number of cores required. The clustering configuration is optimized via simulated annealing to minimize total core usage while maintaining a designer-specified bound on the system-level probability of deadline misses. Experiments on synthetic task sets demonstrate that the proposed method reduces the required number of cores by up to 36% compared to a deterministic baseline, with larger gains for DAGs exhibiting higher internal parallelism.

Cite As Get BibTex

Hiroto Takahashi, Atsushi Yano, and Takuya Azumi. Probabilistic Schedulability Analysis for Mixed-Criticality DAG Tasks on Multiprocessors. In 38th European Conference on Real-Time Systems (ECRTS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 375, pp. 3:1-3:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.ECRTS.2026.3

Author Details

Hiroto Takahashi
  • Graduate School of Science and Engineering, Saitama University, Japan
Atsushi Yano
  • Graduate School of Science and Engineering, Saitama University, Japan
  • TIER IV Inc., Tokyo, Japan
Takuya Azumi
  • Graduate School of Science and Engineering, Saitama University, Japan
  • TIER IV Inc., Tokyo, Japan

Funding

This work was supported by JST CREST Grant Number JPMJCR23M1 and JST FOREST Program Grant Number JPMJFR242G.

Supplementary Materials

References

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