q-AMC: Integrating Quality Management in Mixed Criticality Scheduling

Modern real-time embedded systems increasingly integrate software with varying criticality levels, which increases the interest in mixed criticality scheduling (MCS). MCS provides runtime adaptation mechanisms when low criticality tasks exceed their allocated execution budgets in order to guarantee...

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Bibliographic Details
Published in:Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation) pp. 1 - 8
Main Authors: Boudec, Alan Le, Tran, Hai Nam, Rubini, Stephane, Skrzyniarz, Alexandre, Singhoff, Frank
Format: Conference Proceeding
Language:English
Published: IEEE 09.09.2025
Subjects:
Adaptation models
Computational modeling
Protocols
Quality management
Real-time systems
Runtime
Scheduling
Scheduling algorithms
Software
Timing
ISSN:1946-0759
Online Access:Get full text
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Summary:Modern real-time embedded systems increasingly integrate software with varying criticality levels, which increases the interest in mixed criticality scheduling (MCS). MCS provides runtime adaptation mechanisms when low criticality tasks exceed their allocated execution budgets in order to guarantee the timing constraints of high criticality tasks. Most of the current research on MCS adaptation mechanisms focuses on guaranteeing timing constraints by interrupting and discarding low criticality tasks when their budgets are exceeded. They consider only the temporal dimension, without taking into account the quality of the results obtained. Quality is defined as the accuracy level of the results computed by a task within a given execution time. In this article, we propose an approach to integrate quality in a new task model to establish a relationship between quality and scheduling design. We propose the q-AMC scheduling algorithm to validate our task model. This algorithm integrates quality degradation into the scheduling adaptation mechanism. Simulation-based experiments show that our approach increases the quality up to 44.6% compared to the original AMC approach.
ISSN:1946-0759
DOI:10.1109/ETFA65518.2025.11205611