The mixed-criticality toolbox promises system architects a powerful framework for consolidating real-time tasks with different safety properties on a single computing platform. Thanks to the research efforts in the mixed-criticality field, guarantees provided to the highest criticality level are well understood. However, lower-criticality job execution depends on the condition that all high-criticality jobs complete within their more optimistic low-criticality execution time bounds. Otherwise, no guarantees are made. In this paper, we add to the mixed-criticality toolbox by providing a probabilistic analysis method for low-criticality tasks. While deterministic models reduce task behavior to constant numbers, probabilistic analysis captures varying runtime behavior. We introduce a novel algorithmic approach for probabilistic timing analysis, which we call symbolic scheduling. For restricted task sets, we also present an analytical solution. We use this method to calculate per-job success probabilities for low-criticality tasks, in order to quantify, how low-criticality tasks behave in case of high-criticality jobs overrunning their optimistic low-criticality reservation.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa.de:bsz:14-qucosa-233117 |
| Date | 16 March 2018 |
| Creators | Küttler, Martin, Roitzsch, Michael, Hamann, Claude-Joachim, Völp, Marcus |
| Contributors | Technische Universität Dresden, Fakultät Informatik |
| Publisher | Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:workingPaper |
| Format | application/pdf |
| Relation | dcterms:isPartOf:Technische Berichte / Technische Universität Dresden, Fakultät Informatik; 2017,02 (TUD-FI17-02-November 2017) |
Page generated in 0.0022 seconds