Learning-Based Drop-Aware Mixed-Criticality Task Scheduling

Existing MC scheduling algorithms lead to an underutilized system due to frequent drops of LC tasks and creation of unused slack times due to the quick execution of HC tasks. Accordingly, this chapter proposes a novel optimistic scheme that introduces a learning-based drop-aware task scheduling mechanism, which carefully monitors the alterations in the behavior of the MC system at run-time, to exploit the generated dynamic slacks for reducing the LC tasks’ penalty and preventing frequent drops of LC tasks in the future.