ESOMICS: ML-Based Timing Behavior Analysis for Efficient Mixed-Criticality System Design

Abstract

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.

Keywords

• Machine learning
• mixed-criticality
• resource utilization
• mode switching probability
• WCET analysis