Partitioned Scheduling for DAG Tasks Considering Probabilistic Execution Time

Abstract

Autonomous driving systems, critical for safety, require real-time guarantees and can be modeled as DAGs. Their acceleration features, such as caches and pipelining, often result in execution times below the worst-case. Thus, a probabilistic approach ensuring constraint satisfaction within a probability threshold is more suitable than worst-case guarantees for these systems. This paper considers probabilistic guarantees for DAG tasks by utilizing the results of probabilistic guarantees for single processors, which have been relatively more advanced than those for multi-core processors. This paper proposes a task set partitioning method that guarantees schedulability under the partitioned scheduling. The evaluation on randomly generated DAG task sets demonstrates that the proposed method schedules more task sets with a smaller mean analysis time compared to existing probabilistic schedulability analysis for DAGs. The evaluation also compares four bin-packing heuristics, revealing Item-Centric Worst-Fit-Decreasing schedules the most task sets.