State of the Art for Scheduling and Analyzing Self-Suspending Sporadic Real-Time Tasks

In computing systems, a job/process/task/thread may suspend itself when it has to wait for some other internal or external activities, such as computation offloading or memory accesses, to finish before it can continue its execution. In the literature, there are two commonly adopted self-suspending sporadic task models in real-time systems: 1) the dynamic self-suspension model and 2) the segmented self-suspension sporadic task model. A dynamic self-suspending sporadic task is specified with an upper bound on the maximum suspension time for a job (task instance), which allows a job to dynamically suspend itself arbitrary often as long as the suspension time upper bound is not violated. By contrast, a segmented self-suspending sporadic task has a predefined execution and suspension pattern in an interleaving manner. The dynamic self-suspension model is very flexible but inaccurate, whilst the segmented self-suspension model is very restrictive but very accurate. The gap between these two widely-adopted self-suspension task models can be potentially filled by the hybrid self-suspension task model. The investigation of the impact of self-suspension on timing predictability has been started in 1988. This survey paper provides a short summary of the state of the art in the design and analysis of scheduling algorithms and schedulability tests for self-suspending tasks in real-time systems.