Document VersionPublisher's PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publicationCitation for published version (APA): Bekooij, M. J. G., Hoes, R. J. H., Moreira, O., Poplavko, P., Pastrnak, M., Mesman, B., ... Meerbergen, van, J. (2005). Dataflow analysis for real-time embedded multiprocessor system design. In P. Stok, van der (Ed.), Dynamic and Robust Streaming in and between Connected Consumer-Electronic Devices (pp. 81-108). (Philips research book series; Vol. 3). Dordrecht: Springer.

Modern embedded systems typically contain chip-multiprocessors (CMPs) and support a variety of applications. Applications may run concurrently and can be started and stopped over time. Each application may typically have multiple feasible configurations, trading off quality aspects (energy consumption, audio-visual quality) with resource usage for various types of resources. Overall system quality needs to be guaranteed and optimized at all times. This leads to the need for a run-time management solution that selects an appropriate system configuration from all the application configurations of active applications. This run-time management problem can be phrased as a multi-dimensional multiplechoice knapsack (MMKP) problem. We present a compositional heuristic to solve MMKP, that due to the compositionality is better suited to CMP run-time management than existing heuristics that are all not compositional. Our heuristic outperforms the best-known heuristic to date. The heuristic is parameterized, leading to the additional advantage that it allows to trade off execution time vs. solution quality, and to bound the time needed to compute a solution. The latter makes it particularly well-suited for resource-constrained embedded platforms.

Quality of Service (QoS) support for wireless sensor networks (WSN) is a fairly new topic that is gaining more and more interest. This paper introduces a method for configuring the nodes of a WSN such that application-level QoS constraints are met. This is a complex task, since the search space is typically extremely large. The method is based on a recent algebraic approach to Pareto analysis, that we use to reason about QoS trade-offs. It features an algorithm that keeps the working set of possible configurations small, by analysing parts of the network in a hierarchical fashion, and meanwhile discarding configurations that are inferior to other configurations. Furthermore, we give WSN models for two different applications, in which QoS trade-offs are made explicit. Test results show that the models are accurate and that the method is scalable and thus practically usable for WSN, even with large numbers of nodes.

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