WeaklyHard.jl: Scalable Analysis of Weakly-Hard Constraints

Vreman, Nils; Pates, Richard; Maggio, Martina

doi:10.1109/rtas54340.2022.00026

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…In the remainder of this section, we propose an algorithm to generate a minimal (m, k)-compliant automata A * k (α), which is necessary to improve the computational complexity of our to be designed expected execution time minimization algorithms. We note that the approach to generate minimal finite-state machines as e.g., used in Vreman et al in [43] is applicable for (m, k) constraints as well. However, their generation algorithm is similar to Hopcroft's algorithm [21], which generates all states and merges equivalent states whilst our Algorithm 1 utilizes the specificity of the problem to only generate compliant states right away.…”

Section: B States Reduction and Minimal Automata Constructionmentioning

confidence: 99%

“…Another series of fault tolerance techniques rely on the (m, k) models, which is originally developed for guaranteeing limited deadline misses for firmed real-time systems, or socalled weakly-hard real-time systems [4], where a task has to meet at least m deadlines, or can miss at most m deadlines, in any of k consecutive jobs 2 . Although the original work [4] applied n m to describe the any n of m, most of the following works utilize the (m, k) to describe the weakly-hard constraints [11], [18], [39], [43]. Afterwards, such (m, k) models are commonly applied for fault tolerance related domain as well to describe the robustness constraints of (control) systems.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Average Task Execution Time Minimization under (m, k) Soft Error Constraint

Shi

Ueter

Chen

et al. 2023

2023 IEEE 29th Real-Time and Embedded Technology and Applications Symposium (RTAS)

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Safety-critical systems are often subjected to transient faults. Since these transient faults may lead to soft errors that cause catastrophic consequences, error-handling must be addressed by design. Full-protection against faults is too costly in terms of resource usage. A common approach to relax the resource demands and limit the impact of errors is to consider (m, k)-constraints, which requires that at least m jobs out of any k consecutive jobs are error-free. To assure (m, k)-compliance, static patterns are widely used to select the job execution modes, i.e., either in an error-free mode at the cost of increased worst-case execution time or in an error-prone mode with the advantage of less execution time. Although static patterns have been shown to be effective in energy-aware designs, resource over-provision is inevitable due to the relatively low rate of error probability. In this work, we propose two dynamic (and adaptive) approaches that allow the scheduler to opportunistically select execution modes based on the error-history of the past jobs and the actual error probability. We firstly propose a Markov chain based solution if the error-probability is known and static and secondly a reinforcement learning-based approach that can handle unknown error probabilities. Experimental evaluations show that our approaches outperform the state-of-the-art in most of the evaluated cases in terms of average utilization for each task and the overall utilization for multitask systems.1 2023 IEEE 29th Real-Time and Embedded Technology and Applications Symposium (RTAS)

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Section: B States Reduction and Minimal Automata Constructionmentioning

confidence: 99%