Optimizing Federated Scheduling for Real-Time DAG Tasks via Node-Level Parallelization

Real-time task scheduling in multi-core systems is a crucial research area, especially for parallel task scheduling, where the Directed Acyclic Graph (DAG) model is commonly used to represent task dependencies. However, existing research shows that resource utilization and schedulability rates for DAG task set scheduling remain relatively low. Meanwhile, some studies have identified that certain parallel task nodes exhibit “parallelization freedom,” allowing them to be decomposed into sub-threads that can execute concurrently. This presents a promising opportunity for improving task schedulability. Building on this, we propose an approach that optimizes both node parallelization and processor core allocation under federated scheduling. Simulation experiments demonstrate that by parallelizing nodes, we can significantly reduce the number of cores required for each task and increase the percentage of task sets being schedulable.