ADAPTIVE: Agent-Based Learning for Bounding Time in Mixed-Criticality Systems

In Mixed-Criticality (MC) systems, the high Worst-Case Execution Time (WCET) of a task is a pessimistic bound, the maximum execution time of the task under all circumstances, while the low WCET should be close to the actual execution time of most instances of the task to improve utilization and Quality-of-Service (QoS). Most MC systems consider a static low WCET for each task which cannot adapt to dynamism at run-time. In this regard, we consider the run-time behavior of tasks and propose a learning-based approach that dynamically monitors the tasks' execution times and adapts the low WCETs to determine the ideal trade-off between mode-switches, utilization, and QoS. Based on our observations on running embedded real-time benchmarks on a real platform, the proposed scheme improves the QoS by 16.4% on average while reducing the utilization waste by 17.7%, on average, compared to state-of-the-art works.