Real-time scheduling of parallel tasks with tight deadlines

Real-time systems are shifting from single-core to multi-core processors, on which software must be parallelized to fully utilize their computation power. Recently, different types of scheduling algorithms have been proposed for parallel real-time tasks modeled as directed acyclic graphs (DAG), among which federated scheduling shows its superiority in real-time performance. However, the performance of federated scheduling seriously degrades for tasks with tight relative deadlines (the gap between the relative deadline and the longest path length is small). In this paper, we propose new methods based on federated scheduling to solve this problem by exploring the intra-task structure information. By our new methods, each heavy task is transformed into a set of independent sporadic sub-tasks with the guidance of its intra-task structure information, such that the number of processors required is reduced. We conduct experiments to evaluate our proposed approach against the state-of-the-art methods of different types of scheduling algorithms. Experimental results show that our approach consistently outperforms all of the compared methods under different parameter settings, especially for task sets consisting of tasks with tight deadlines.