  In this work, we propose to integrate prediction algorithms to the scheduling
of mode changes under the Earliest-Deadline-First and Fixed-priority scheduling
in mixed-criticality real-time systems. The method proactively schedules a mode
change in the system based on state variables such as laxity, to the percentage
difference in the temporal distance between the completion time of the instance
of a task and its respective deadline, by the deadline (D) stipulated for the
task, in order to minimize deadline misses. The simulation model was validated
against an analytical model prior to the logical integration of the
Kalman-based prediction algorithm. Two study cases were presented, one covering
earliest-deadline first and the other the fixed-priority scheduling approach.
The results showed the gains in the adoption of the prediction approach for
both scheduling paradigms by presenting a significant reduction of the number
of missed deadlines for low-criticality tasks.
