An Elastic Mixed-Criticality Task Model and Early-Release EDF Scheduling Algorithms

Many algorithms have recently been studied for scheduling mixed-criticality (MC) tasks. However, most existing MC scheduling algorithms guarantee the timely executions of high-criticality (HC) tasks at the expense of discarding low-criticality (LC) tasks, which can cause serious service interruption for such tasks. In this work, aiming at providing guaranteed services for LC tasks, we study an elastic mixed-criticality (E-MC) task model for dual-criticality systems. Specifically, the model allows each LC task to specify its maximum period (i.e., minimum service level) and a set of early-release points. We propose an early-release (ER) mechanism that enables LC tasks to be released more frequently and thus improve their service levels at runtime, with both conservative and aggressive approaches to exploiting system slack being considered, which is applied to both earliest deadline first (EDF) and preference-oriented earliest-deadline schedulers. We formally prove the correctness of the proposed early-release--earliest deadline first scheduler on guaranteeing the timeliness of all tasks through judicious management of the early releases of LC tasks. The proposed model and schedulers are evaluated through extensive simulations. The results show that by moderately relaxing the service requirements of LC tasks in MC task sets (i.e., by having LC tasks’ maximum periods in the E-MC model be two to three times their desired MC periods), most transformed E-MC task sets can be successfully scheduled without sacrificing the timeliness of HC tasks. Moreover, with the proposed ER mechanism, the runtime performance of tasks (e.g., execution frequencies of LC tasks, response times, and jitters of HC tasks) can be significantly improved under the ER schedulers when compared to that of the state-of-the-art earliest deadline first—virtual deadline scheduler.