An Elastic Mixed-Criticality Task Model And Its Scheduling Algorithm

To address the service abrupt problem for low-criticality tasks in existing mixed-criticality scheduling algorithms, we study an Elastic Mixed-Criticality (E-MC) task model, where the key idea is to have variable periods (i.e., service intervals) for low-criticality tasks. The minimum service requirement of a low-criticality task is ensured by its largest period. However, at runtime low-criticality tasks can be released early by exploiting the slack time generated from the over-provisioned execution time for high-criticality tasks to reduce their service intervals and thus improve their service levels. We propose an Early-Release EDF (ER-EDF) scheduling algorithm, which can judiciously manage the early release of low-criticality tasks without affecting the timeliness of high-criticality tasks. Compared to the state-of-the-art EDF-VD scheduling algorithm, our simulation results show that the ER-EDF can successfully schedule much more task sets. Moreover, the achieved execution frequencies of low-criticality tasks can also be significantly improved under ER-EDF.