Abstract
Mixing workloads with multiple criticality levels raises challenges both in timing analysis and schedulability analysis. The timing models have to characterize the different behaviors that real-time tasks can experience under the various criticality modes. Instead, the schedulability analysis has to combine every task and task interactions providing several guarantees, depending on the criticality level demanded at runtime. With this work, at first, we propose representations to model every possible system criticality mode as a combination of task criticality modes. A set of bounding functions is obtained, a bound for each mode combination thus corresponding to a system criticality level. Secondly, we develop the schedulability analysis that applies such sets and derives schedulability conditions with mixed criticalities. The tasks are scheduled with fixed priority and earlies deadline first, and various levels of schedulability are defined from the mode combinations. Finally, we make use of the sensitivity analysis to evaluate the impact that multi-mode task behaviors have on schedulability. Trade-offs between schedulability, criticality levels and resource availability are explored. A mixed critical real-time system case study validates the framework proposed.
Recommended Citation
L. Santinelli and Z. Guo, "A Sensitivity Analysis for Mixed Criticality: Trading Criticality with Computational Resource," IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, pp. 313 - 320, article no. 8502493, Institute of Electrical and Electronics Engineers, Oct 2018.
The definitive version is available at https://doi.org/10.1109/ETFA.2018.8502493
Department(s)
Computer Science
International Standard Book Number (ISBN)
978-153867108-5
International Standard Serial Number (ISSN)
1946-0759; 1946-0740
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
22 Oct 2018
Comments
National Science Foundation, Grant 1850851