Abstract
The logical execution time (LET) model separates logical from physical execution times. Furthermore, tasks’ input and output of data occurs at predictable times that are the tasks’ arrival times and deadlines, respectively. The output of data is delayed until the period end meaning that output times have no jitter. The delayed output affects the freshness of data (respectively data age) between interacting tasks. Recently, critics from control theory arise that the LET approach provides outdated data. We analyze the data age of communicating tasks and propose an approach that reduces the data age. Therefore, we reduce the LET of tasks such that output data is provided earlier than at a task’s deadline, but still preserve the predictability of output times. To confirm the improvement on the data age, we simulate 100 randomly generated task sets. Moreover, we also simulate a task set of a real-world automotive benchmark and show an enhancement of the average data age of approximately 33 % with our approach compared to the LET model.
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Bradatsch, C., Kluge, F., Ungerer, T. (2016). Data Age Diminution in the Logical Execution Time Model. In: Hannig, F., Cardoso, J.M.P., Pionteck, T., Fey, D., Schröder-Preikschat, W., Teich, J. (eds) Architecture of Computing Systems – ARCS 2016. ARCS 2016. Lecture Notes in Computer Science(), vol 9637. Springer, Cham. https://doi.org/10.1007/978-3-319-30695-7_13
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DOI: https://doi.org/10.1007/978-3-319-30695-7_13
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