Motivating the Use of Machine-Learning for Improving Timing Behaviour of Embedded Mixed-Criticality Systems

In Mixed-Criticality (MC) systems, due to encoun-tering 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 a novel scheme to obtain appropriate WCET for LO modes. 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 % for the ML-based approach, while mode switching probability is bounded by 7.19 % in the worst-case scenario.