On the Criticality of Probabilistic Worst-Case Execution Time Models
Probabilistic approaches to timing analysis derive probability distributions to upper bound task execution time. The main purpose of probability distributions instead of deterministic bounds, is to have more flexible and less pessimistic worst-case models. However, in order to guarantee safe probabilistic worst-case models, every possible execution condition needs to be taken into account.
In this work, we propose probabilistic representations which is able to model every task and system execution conditions, included the worst-cases. Combining probabilities and multiple conditions offers a flexible and accurate representation that can be applied with mixed-critical task models and fault effect characterizations on task executions. A case study with single- and multi-core real-time systems is provided to illustrate the completeness and versatility of the representation framework we provide.
L. Santinelli and Z. Guo, "On the Criticality of Probabilistic Worst-Case Execution Time Models," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10606 LNCS, pp. 59-74, Springer Verlag, Oct 2017.
The definitive version is available at https://doi.org/10.1007/978-3-319-69483-2_4
3rd International Symposium on Dependable Software Engineering: Theories, Tools and Applications, SETTA 2017 (2017: Oct. 23-25, Changsha, China)
Keywords and Phrases
Critical Tasks; Fault Effect; Probabilistic Approaches; Probabilistic Representation; Task Executions; Timing Analysis; Worst-Case Execution Time; Worst-Case Models; Application Programs; Interactive Computer Systems; Probability; Real Time Systems; Software Engineering
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