In mixed criticality systems (MCSs), the time-triggered scheduling approach focuses on a special case of safety-critical embedded applications which run in a time-triggered environment. Sometimes, for these types of MCSs, perfectly periodical (i.e., jitterless) scheduling for certain critical tasks is needed. In this paper, we propose FENP_MC (Fixed Execution Non-Preemptive Mixed Criticality), a real-time, table-driven, non-preemptive scheduling method specifically adapted to mixed criticality systems which guarantees jitterless execution in a mixed criticality time-triggered environment. We also provide a multiprocessor version, namely, P_FENP_MC (Partitioned Fixed Execution Non-Preemptive Mixed Criticality), using a partitioning heuristic. Feasibility tests are proposed for both uniprocessor and homogenous multiprocessor systems. An analysis of the algorithm performance is presented in terms of success ratio and scheduling jitter by comparing it against a time-triggered and an event-driven method in a non-preemptive context.
The Real-Time Internet of Things is an emerging technology intended to enable real-time information communication and processing over a global network of devices at the edge level. Given the lessons learned from general real-time systems, where the mixed-criticality scheduling concept has proven to be an effective approach for complex applications, this paper formalizes the paradigm of the Mixed-Criticality Internet of Things. In this context, the evolution of real-time scheduling models is presented, reviewing all the key points in their development, together with some connections between different models. Starting from the classical mixed-criticality model, a mathematical formalization of the Mixed-Criticality Internet of Things concept, together with a specifically tailored methodology for scheduling mixed-criticality applications on IoT nodes at the edge level, is presented. Therefore, a novel real-time hardware-aware task model for distributed mixed-criticality systems is proposed. This study also offers a model for setting task parameters based on an IoT node-related affinity score, evaluates the proposed mapping algorithm for task scheduling, and presents some use cases.
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