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Model-based partition scheduling of integrated modular avionics systems using genetic algorithm.

Jichen Chen1, Zhengjun Zhai2, Pujie Han3

  • 1School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China.

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|August 26, 2025
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Summary
This summary is machine-generated.

This study optimizes partition scheduling in Integrated Modular Avionics (IMA) systems using a model-based approach. The method enhances processor utilization and efficiency for real-time systems adhering to ARINC 653 standards.

Keywords:
Genetic algorithmIntegrated modular avionicsPartition schedulingTimed automataUPPAAL

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Area of Science:

  • Computer Science
  • Real-Time Systems Engineering
  • Aerospace Systems

Background:

  • Integrated Modular Avionics (IMA) systems are crucial for modern aircraft, requiring efficient resource management.
  • ARINC 653 compliance presents complex scheduling challenges, particularly with multi-core processors and diverse partition types.
  • Traditional methods struggle with the combinatorial complexity of optimizing real-time partition scheduling.

Purpose of the Study:

  • To develop a model-based method for optimizing partition scheduling in ARINC 653 compliant IMA systems.
  • To maximize processor utilization while ensuring real-time constraints are met.
  • To improve the efficiency and applicability of partition scheduling in IMA design.

Main Methods:

  • Utilizing timed automata in UPPAAL for modeling ARINC 653 features like two-level scheduling and multi-core processors.
  • Employing a parallel genetic algorithm to explore the scheduling parameter space efficiently.
  • Implementing a compositional framework for independent partition verification and global schedulability analysis.

Main Results:

  • The proposed method achieves lower processor occupancy compared to traditional techniques.
  • Demonstrated improved optimization performance in scheduling real-time partitions.
  • Successfully mitigated state space explosion issues common in model checking.

Conclusions:

  • The model-based approach offers a more efficient and practical solution for partition optimization in IMA systems.
  • The compositional framework enhances the scalability of schedulability analysis.
  • This work contributes to more efficient real-time system design in avionics.