Optimal soft real-time semi-partitioned scheduling made simple (and dynamic)

Hobbs, Clara; Tong, Zelin; Anderson, James H.

doi:10.1145/3356401.3356402

Cited by 9 publications

(1 citation statement)

References 24 publications

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“…Moreover, the worst-case parameter inference is increasingly complex and pessimistic for parallel architectures further bolstering the importance of probabilistic models and analyses. For sequential stochastic tasks a plethora of prior work concerning probabilistic analyses exists, e.g., [17], [30]. Recent work focused on the improvements of efficiency in convolution-based probabilistic deadline-miss analysis approaches.…”

Section: Related Workmentioning

confidence: 99%

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

Ueter¹,

Günzel²,

Chen³

2021

Preprint

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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.

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Section: Related Workmentioning

confidence: 99%