Type-Aware Federated Scheduling for Typed DAG Tasks on Heterogeneous Multicore Platforms

Abstract

To utilize the performance benefits of heterogeneous multicore platforms in real-time systems, we need task models that expose the parallelism and heterogeneity of the workload, such as typed DAG tasks, as well as scheduling algorithms that effectively exploit this information. In this paper, we introduce <i>type-aware federated scheduling</i> algorithms for sporadic typed DAG tasks with implicit deadlines running on a heterogeneous multicore platform with two different types of cores. In type-aware federated scheduling, a task can be executed in one of the three strategies: <i>Exclusive Allocation</i>, <i>Semi-Exclusive Allocation</i>, and <i>Sequential and Share</i>. In <i>Exclusive Allocation</i>, clusters of cores of both core types are exclusively allocated to tasks, while cores of only one type are exclusively allocated to tasks in <i>Semi-Exclusive Allocation</i>. The workload of the other type from tasks in <i>Semi-Exclusive Allocation</i> and the workload from tasks in <i>Sequential and Share</i> share the cores that are not exclusively allocated to any task. We prove that our type-aware federated scheduling algorithm has a capacity augmentation bound of 7.25. We also show that no constant capacity augmentation bound can be obtained without <i>Semi-Exclusive Allocation</i>. Compared to the state of the art, the type-aware federated scheduling algorithm achieves better schedulability, especially for task sets with skewed workload.