Algorithms for implementing elastic tasks on multiprocessor platforms: a comparative evaluation

The elastic task model enables the adaptation of systems of recurrent real-time tasks under uncertain or potentially overloaded conditions. A range of permissible periods is specified for each task in this model; during run-time a period is selected for each task from the specified range of permissible periods to ensure schedulability in a manner that maximizes the quality of provided service. This model was originally defined for sequential tasks executing upon a preemptive uniprocessor platform; here we consider the implementation of sequential tasks upon multiprocessor platforms. We define algorithms for scheduling sequential elastic tasks under the global and partitioned paradigms of multiprocessor scheduling for both dynamic and static-priority tasks, and we provide an extensive simulation-based comparison of the different approaches.

Automated summary

The elastic task model allows systems to adapt recurrent real-time tasks under uncertain or potentially overloaded conditions by specifying a range of permissible periods for each task. A period is selected for each task at run-time to maximize the quality of service provided while ensuring schedulability. Originally defined for sequential tasks on a preemptive uniprocessor platform, the model is now being implemented for sequential tasks on multiprocessor platforms with algorithms designed for both global and partitioned paradigms. Extensive simulation-based comparisons of different approaches are provided for dynamic and static-priority tasks.