NDLI: IVaM: implicit variant modeling and management for automotive embedded systems

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IVaM: implicit variant modeling and management for automotive embedded systems

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IVaM: implicit variant modeling and management for automotive embedded systems

Content Provider	ACM Digital Library
Author	Lauer, Christoph Graf, Sebastian Teich, Jürgen Wintermann, Dominic Glaß, Michael
Abstract	In this paper, we propose a graph-based approach for the modeling and efficient analysis of functional variants of a car's electric and electronic (E/E) architecture functionality by combining local technical expert knowledge with global business knowledge. Starting with a variants system specification including a set of task graphs, linear constraints on binary variables are specified for their alternative selection as well as the selection of groups of alternatives called application groups. These constraints may stem from a certain domain knowledge, e.g., entertainment or power train domain, or global constraints. The typically vast space of resulting possible combinations of different selections of alternative behaviors will be termed variant space and those satisfying the set of formulated constraints valid variants. An important result of this paper is that the set of variants, and especially the set of valid variants, do not need to be modeled or stored explicitly but rather implicitly. Nevertheless do we show that using state-of-the-art PB solver techniques, we may determine the set of valid variants very efficiently. Each of these valid variants may subsequently be used as a candidate for design space exploration (DSE) in order to optimize also the mapping of the corresponding task graph functionalities to a final optimized E/E architecture. A real-world case study is provided to demonstrate the capabilities and efficiency of the presented approach on implicit variant modeling and analysis.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781479914173
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-09-29
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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