Response-Time Analysis and Optimization for Probabilistic Conditional Parallel DAG Tasks

Cyber-physical systems (CPS) increasingly use multicore processors in order to satisfy power and computational requirements. To exploit the architectural parallelism offered by the multicore processors, parallel task models and appropriate scheduling algorithms have to be provided. Directed-acyclic graphs (DAGs) are prominent models to express parallelism and precedence constraints. In classic real-time systems, all tasks have to comply with strict timing constraints, which however result in resource underutilization due to pessimistic assumptions. Applications in CPS that have traditionally been considered as hard real-time such as control algorithms have demonstrated inherent robustness that can tolerate occasional deadline misses. In this paper, we propose a hierarchical scheduling algorithm and probabilistic response-time analyses for probabilistic conditional DAG tasks that allow to guarantee a bounded probability for k consecutive deadline misses without enforcing late jobs to be immediately aborted.