ESOMICS: ML-Based Timing Behavior Analysis for Efficient Mixed-Criticality System Design
In Mixed-Criticality (MC) systems, due to encountering multiple Worst-Case Execution Times (WCETs) for each task corresponding to the system operation modes, estimating appropriate WCETs for tasks in lower-criticality (LO) modes is essential to improve the system’s timing behavior. While...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10517595/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850101380234084352 |
|---|---|
| author | Vikash Kumar Behnaz Ranjbar Akash Kumar |
| author_facet | Vikash Kumar Behnaz Ranjbar Akash Kumar |
| author_sort | Vikash Kumar |
| collection | DOAJ |
| description | In Mixed-Criticality (MC) systems, due to encountering multiple Worst-Case Execution Times (WCETs) for each task corresponding to the system operation modes, estimating appropriate WCETs for tasks in lower-criticality (LO) modes is essential to improve the system’s timing behavior. While numerous studies focus on determining WCET in the high-criticality mode, determining the appropriate WCET in the LO mode poses significant challenges and has been addressed in a few research works due to its inherent complexity. This article introduces ESOMICS, a novel scheme, to obtain appropriate WCET for LO modes, in which we propose an ML-based approach for WCET estimation based on the application’s source code analysis and the model training using a comprehensive data set. The experimental results show a significant improvement in utilization by up to 23.3% compared to state-of-the-art works, while mode switching probability is bounded by 7.19%, in the worst-case scenario. |
| format | Article |
| id | doaj-art-befb9c25aa7a45de8bc270d37ec3dccb |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-befb9c25aa7a45de8bc270d37ec3dccb2025-08-20T02:40:03ZengIEEEIEEE Access2169-35362024-01-0112670136702410.1109/ACCESS.2024.339622510517595ESOMICS: ML-Based Timing Behavior Analysis for Efficient Mixed-Criticality System DesignVikash Kumar0Behnaz Ranjbar1https://orcid.org/0000-0001-7944-7101Akash Kumar2https://orcid.org/0000-0001-7125-1737Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, Bengaluru, IndiaChair of Embedded Systems, Ruhr University Bochum, Bochum, GermanyChair of Embedded Systems, Ruhr University Bochum, Bochum, GermanyIn Mixed-Criticality (MC) systems, due to encountering multiple Worst-Case Execution Times (WCETs) for each task corresponding to the system operation modes, estimating appropriate WCETs for tasks in lower-criticality (LO) modes is essential to improve the system’s timing behavior. While numerous studies focus on determining WCET in the high-criticality mode, determining the appropriate WCET in the LO mode poses significant challenges and has been addressed in a few research works due to its inherent complexity. This article introduces ESOMICS, a novel scheme, to obtain appropriate WCET for LO modes, in which we propose an ML-based approach for WCET estimation based on the application’s source code analysis and the model training using a comprehensive data set. The experimental results show a significant improvement in utilization by up to 23.3% compared to state-of-the-art works, while mode switching probability is bounded by 7.19%, in the worst-case scenario.https://ieeexplore.ieee.org/document/10517595/Machine learningmixed-criticalityresource utilizationmode switching probabilityWCET analysis |
| spellingShingle | Vikash Kumar Behnaz Ranjbar Akash Kumar ESOMICS: ML-Based Timing Behavior Analysis for Efficient Mixed-Criticality System Design IEEE Access Machine learning mixed-criticality resource utilization mode switching probability WCET analysis |
| title | ESOMICS: ML-Based Timing Behavior Analysis for Efficient Mixed-Criticality System Design |
| title_full | ESOMICS: ML-Based Timing Behavior Analysis for Efficient Mixed-Criticality System Design |
| title_fullStr | ESOMICS: ML-Based Timing Behavior Analysis for Efficient Mixed-Criticality System Design |
| title_full_unstemmed | ESOMICS: ML-Based Timing Behavior Analysis for Efficient Mixed-Criticality System Design |
| title_short | ESOMICS: ML-Based Timing Behavior Analysis for Efficient Mixed-Criticality System Design |
| title_sort | esomics ml based timing behavior analysis for efficient mixed criticality system design |
| topic | Machine learning mixed-criticality resource utilization mode switching probability WCET analysis |
| url | https://ieeexplore.ieee.org/document/10517595/ |
| work_keys_str_mv | AT vikashkumar esomicsmlbasedtimingbehavioranalysisforefficientmixedcriticalitysystemdesign AT behnazranjbar esomicsmlbasedtimingbehavioranalysisforefficientmixedcriticalitysystemdesign AT akashkumar esomicsmlbasedtimingbehavioranalysisforefficientmixedcriticalitysystemdesign |