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Probabilistic worst case response time estimation for multi-core real-time systems

  • Traditional methods rely on Static Timing Analysis techniques to compute the Worst Case Response Time for tasks in real-time systems. Multi-Core real-time systems are faced up with concurrent task executions, semaphore accesses, and task migrations where it may be difficult to obtain the worst case upper bound. A new three staged probabilistic estimation concept is presented. Worst Case Response Times are estimated for tasksets which consist of tasks with multiple time bases. The concept involves data generation with sample classification and sample size equalization, model fit and Worst Case Response Time estimation on the basis of extreme value distribution models. A Generalized Pareto Distribution model fit method which includes threshold detection and parameter estimation is also presented. Sample classification in combination with the new Generalized Pareto Distribution model fit method allows to estimate Worst Case Response Times with low pessimism ranges compared to estimation methods that uses the Generalized Pareto or the Gumbel max distribution without sample classification.

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Metadaten
Author:Matthias Mucha, Jürgen MottokORCiDGND, Michael Deubzer
Parent Title (English):2015 4th Mediterranean Conference on Embedded Computing (MECO), 14-18 June 2015, Budva, Montenegro
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2015
Release Date:2021/03/11
First Page:31
Last Page:36
Institutes:Fakultät Elektro- und Informationstechnik
Fakultät Elektro- und Informationstechnik / Laboratory for Safe and Secure Systems (LAS3)
research focus:Digitalisierung