Abstract
Multi-valued Decision Diagrams (MDDs) are used in various fields of application. In performance evaluation, a compact representation of the state space of Markovian systems can often be achieved by using MDDs. It is well known that the size of the resulting MDD representation heavily depends on the variable ordering, i.e. the arrangement of the levels within the MDD. Markov models, derived from higher level descriptions of the system, often contain structural information. This information might give hints for an optimized variable ordering a priori, i.e. before the MDD is constructed. Whenever a model is described by constraints—considering the design space of a system, for example—there is a lack of such structural information. This is the reason why the MDD representation often consumes too much memory to be handled efficiently. In order to keep the memory consumption practicable, we have developed two optimization mechanisms. The presented examples demonstrate that efficient MDD representations of the feasible design space can be obtained, even for large unstructured systems.
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Berndt, R., Bazan, P., Hielscher, KS., German, R. (2014). Construction Methods for MDD-Based State Space Representations of Unstructured Systems. In: Fischbach, K., Krieger, U.R. (eds) Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance. MMB&DFT 2014. Lecture Notes in Computer Science, vol 8376. Springer, Cham. https://doi.org/10.1007/978-3-319-05359-2_4
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DOI: https://doi.org/10.1007/978-3-319-05359-2_4
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