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

arXiv: https://arxiv.org/abs/2101.11053 · HTML: https://mlsanity.com/paper/2101.11053/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2021.

## Built on by (most cited first, 1 of 1)

- [Edge Generation Scheduling for DAG Tasks Using Deep Reinforcement Learning](https://mlsanity.com/paper/2308.14647/) (2023)

## Abstract (arXiv)

Real-time systems increasingly use multicore processors in order to satisfy thermal, power, and computational requirements. To exploit the architectural parallelism offered by the multicore processors, parallel task models, scheduling algorithms and response-time analyses with respect to real-time constraints have to be provided. In this paper, we propose a reservation-based scheduling algorithm for sporadic constrained-deadline parallel conditional DAG tasks with probabilistic execution behaviour for applications that can tolerate bounded number of deadline misses and bounded tardiness. We devise design rules and analyses to guarantee bounded tardiness for a specified bounded probability for k-consecutive deadline misses without enforcing late jobs to be immediately aborted.