A New Federated Scheduling Algorithm for Arbitrary-Deadline DAG Tasks

A parallel task can always be modelled as a directed acyclic graph (DAG), where sequential instruction blocks are modelled as vertices and data dependencies or resource constraints are modelled as edges. We propose a new federated scheduling algorithm for arbitrary-deadline sporadic DAG tasks, assuming that the exact structures of DAG tasks are unknown before runtime. Federated scheduling algorithms are a class of algorithms that can efficiently schedule DAG tasks by assigning several processors exclusively to each task. Existing studies have shown the advantages of federated scheduling, which include increasing the analytical schedulability and minimising the scheduling overhead. We are particularly focused on the scheduling of any task with a deadline longer than its release period; in this case, multiple jobs generated by the task could run concurrently. For such tasks, our algorithm is different from most federated scheduling algorithms in that it assigns dedicated processors to each job instead of letting jobs released by the same task share processors. The main idea is to increase the analytical schedulability by avoiding interference between jobs. The simulation results show that our algorithm outperforms existing algorithms when the exact structures of tasks are unknown before runtime.