Execution time budget assignment for mixed criticality systems
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916085028093952 |
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| author | Khelassi, Mohamed Amine Abdeddaïm, Yasmina |
| author_facet | Khelassi, Mohamed Amine Abdeddaïm, Yasmina |
| contents | In this paper we propose to quantify execution time variability of programs using statistical dispersion parameters. We show how the execution time variability can be exploited in mixed criticality real-time systems. We propose a heuristic to compute the execution time budget to be allocated to each low criticality real-time task according to its execution time variability. We show using experiments and simulations that the proposed heuristic reduces the probability of exceeding the allocated budget compared to algorithms which do not take into account the execution time variability parameter. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02431 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Execution time budget assignment for mixed criticality systems Khelassi, Mohamed Amine Abdeddaïm, Yasmina Performance Distributed, Parallel, and Cluster Computing Machine Learning In this paper we propose to quantify execution time variability of programs using statistical dispersion parameters. We show how the execution time variability can be exploited in mixed criticality real-time systems. We propose a heuristic to compute the execution time budget to be allocated to each low criticality real-time task according to its execution time variability. We show using experiments and simulations that the proposed heuristic reduces the probability of exceeding the allocated budget compared to algorithms which do not take into account the execution time variability parameter. |
| title | Execution time budget assignment for mixed criticality systems |
| topic | Performance Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2401.02431 |