Abstract
Executing multiple applications on a multi-core system while the workload of all applications varies brings the challenge of dynamically adapting resource allocations and parametrizing with respect to constraints e.g. timing limits of real-time applications. We present a hybrid approach which extracts a set of Pareto-optimal operating points during design time which are used to dynamically parameterize the periodic application during run-time. The setup is done at the beginning of each iteration of the execution and exclusively allocates processing elements from the system depending on the current workload. The parametrization is performed with the observed information about workload complexity and allocated resources. Therefore guarantees on time limits can be granted for all iterations including situations when the number of available processing elements has been decreased sharply.
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This work was partly supported by the German Research Foundation (DFG), Projectnumber 146371743, TRR 89 Invasive Computing.
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Gabriel, D., Stechele, W., Wildermann, S. (2019). Resource-Aware Parameter Tuning for Real-Time Applications. In: Schoeberl, M., Hochberger, C., Uhrig, S., Brehm, J., Pionteck, T. (eds) Architecture of Computing Systems – ARCS 2019. ARCS 2019. Lecture Notes in Computer Science(), vol 11479. Springer, Cham. https://doi.org/10.1007/978-3-030-18656-2_4
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