Preemption-Aware Allocation, Deadline Assignment for Conditional DAGs on Partitioned EDF

• Published in: 2020 IEEE 26th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA) pp. 1 - 10
• Main Authors: ZAHAF, Houssam-Eddine, Lipari, Giuseppe, Niar, Smail, Hassan Benyamina, Abou El
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2020
• Subjects: Computational modeling; Computer architecture; Processor scheduling; Real-time systems; Resource management; Task analysis; Time factors
• ISSN: 2325-1301
• DOI: 10.1109/RTCSA50079.2020.9203643

Heterogeneous hardware platforms are often used for implementing complex critical real-time applications, like Advanced driver-assistance systems (ADAS) and autonomous driving. Typically, they are composed of CPU hosts and a set of accelerators. To better support real-time workloads, several hardware accelerators have evolved to allow preemption for computationally intensive tasks, such as GPUs. However, their preemption costs can be very high compared to classical CPU preemption, and therefore must be taken into account at design time and in the scheduling analysis. In this paper, we address mainly two tightly correlated problems: (i) task allocation for a set of real-time tasks, modeled by conditional directed acyclic graphs (C-DAG), onto multiprocessor platforms under partitioned preemptive Earliest Deadline First scheduling, assuming a non-negligible cost of preemption, and (ii) intermediate deadlines and offsets assignments to real-time C-DAGs, so to remove unnecessary preemption and reduce the total preemption overhead. The effectiveness of the proposed technique is evaluated using a large set of synthetic tasks sets.