Hybrid self-suspension models in real-time embedded systems

To tackle the unavoidable self-suspension behavior due to I/O-intensive interactions, multi-core processors, computation offloading systems with coprocessors, etc., the dynamic and the segmented self-suspension sporadic task models have been widely used in the literature. We propose new self-suspension models that are hybrids of the dynamic and the segmented models. Those hybrid models are capable of exploiting knowledge about execution paths, potentially reducing modelling pessimism. In addition, we provide the corresponding schedulability analysis under fixed-relative-deadline (FRD) scheduling and explain how the state-of-the-art FRD scheduling strategy can be applied. Empirically, these hybrid approaches are shown to be effective with regards to the number of schedulable task sets.