Real-Time Scheduling of Parallel Tasks under a General DAG Model

Due to their potential to deliver increased performance over single-core processors, multi-core processors have become mainstream in processor design. Computation-intensive real-time systems must exploit intra-task parallelism to take full advantage of multi-core processing. However, existing results in real-time scheduling of parallel tasks focus on restrictive task models such as the synchronous model where a task is a sequence of alternating parallel and sequential segments, and parallel segments have threads of execution that are of equal length. In this paper, we address a general model for deterministic parallel tasks, where a task is represented as a DAG with different nodes having different execution requirements. We make several key contributions towards both preemptive and non-preemptive realtime scheduling of DAG tasks on multi-core processors. First, we propose a task decomposition that splits a DAG into sequential tasks. Second, we prove that parallel tasks, upon decomposition, can be scheduled using preemptive global EDF with a resource augmentation bound of 4. This bound is as good as the best known bound for more restrictive models, and is the first for a general DAG model. Third, we prove that the decomposition has a resource augmentation bound of 4 plus a non-preemption overhead for non-preemptive global EDF scheduling. To our knowledge, this is the first resource augmentation bound for nonpreemptive scheduling of parallel tasks. Through simulations, we demonstrate that the achieved bounds are safe and sufficient.