Robust Scheduling of Task Graphs under Execution Time Uncertainty

Effective multicore computing requires to make efficient usage of the computational resources on a chip. Off-line mapping and scheduling can be applied to improve the performance, but classical approaches require considerable a-priori knowledge of the target application. In a practical setting, precise information is often unavailable; one can then resort to approximate time and resource usage figures, but this usually requires to make conservative assumptions. The issue is further stressed if real-time guarantees must be provided. We tackle predictable and efficient non-preemptive scheduling of multi-task applications in the presence of duration uncertainty. Hard real-time guarantees are provided with limited idle time insertion, by exploiting a hybrid off-line/on-line technique known as Precedence Constraint Posting (PCP). Our approach does not require probability distributions to be specified, relying instead on simple and cheaper-to-obtain information (bounds, average values). The method has been tested on synthetic applications/platforms and compared with an off-line optimized Fixed Priority Scheduling (FPS) approach and a pure on-line FIFO scheduler; the results are very promising, as the PCP schedules exhibit good stability and improved average execution time (14% on average, up to 30% versus FPS and up to 40% versus the FIFO scheduler).