DAG Scheduling with Execution Groups

In many modern safety-critical cyber-physical systems, such as in the automotive or robotic domain, the application complexity requires the use of multi-core platforms to execute all workloads under strict hard real-time constraints. The sporadic DAG task model is a parallel task model adept at representing tasks comprised of subtasks, which possess internal data flow and precedence constraints induced by synchronization. A significant challenge to the system's performance and its real-time verification stems from the communication-centric nature of applications in these domains. Inter-core communication, required for data sharing among subtasks across different cores, depends on either a shared bus or a network-on-chip, culminating in significant overhead due to latency, congestion, and synchronization. To improve performance and reduce these overheads, it is advantageous to execute subtasks, those that either exchange large volumes of data or access the same data, on a singular physical processor, thereby utilizing more efficient intra-core communication. In this paper, we tackle this issue by introducing the DAG task model with execution groups, incorporating a constraint that mandates the execution of grouped subtasks on the same processor. We provide an analysis of worst-case response times and propose optimizations for our DAG task model with execution groups, subsequently evaluating our approach against existing solutions. The evaluation results demonstrate that our approach, even with the imposition of group execution constraints, remains competitive in comparison to existing approaches that do not take group execution constraints into account. Additionally, we explore implementation strategies and potential extensions for multi-task systems.