Network Performance Evaluation for Distributed Embedded Systems Using Feature Models

In this paper, we focus on networked, embedded systems which may contain numerous electronic control units, connected by multiple network busses. Furthermore, such embedded systems support many runtime configurations. The main problem is to determine the network resource needs of all variations permitted at runtime, i.e. to calculate the worst case resource needs. We describe the runtime variability of such systems by means of a runtime feature model and then derive a network performance model in a stepwise way. The mappings from the feature level leads to a data flow model, then to the component/hardware level, where we perform a detailed network analysis based on Network Calculus. Thus, we can decide in an early design stage, whether a given network topology fits the requirements of a given software architecture. We show then, by an example, (i) that the performance of this component/hardware model can be analyzed using network calculus and (ii) that our approach can significantly reduce resource overestimation compared to a static evaluation.

[1]  Eric Rondeau,et al.  Strict priority versus weighted fair queueing in switched Ethernet networks for time critical applications , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[2]  Dorina C. Petriu,et al.  Software Performance Modeling , 2012, SFM.

[3]  Lars Völker,et al.  Challenges in a future IP/Ethernet-based in-car network for real-time applications , 2011, 2011 48th ACM/EDAC/IEEE Design Automation Conference (DAC).

[4]  Nelly Bencomo,et al.  Supporting the modelling and generation of reflective middleware families and applications using dynamic variability , 2008 .

[5]  Gunter Saake,et al.  Interoperability of non-functional requirements in complex systems , 2012, 2012 Second International Workshop on Software Engineering for Embedded Systems (SEES).

[6]  Marek Hatala,et al.  Automated planning for feature model configuration based on functional and non-functional requirements , 2012, SPLC '12.

[7]  Andreas Classen,et al.  A formal semantics for feature cardinalities in feature diagrams , 2011, VaMoS '11.

[8]  Krzysztof Czarnecki,et al.  Formalizing cardinality-based feature models and their specialization , 2005, Softw. Process. Improv. Pract..

[9]  Jean-Marc Jézéquel,et al.  Modeling the Variability Space of Self-Adaptive Applications , 2008, SPLC.

[10]  Pierre-Yves Schobbens,et al.  Feature Diagrams: A Survey and a Formal Semantics , 2006, 14th IEEE International Requirements Engineering Conference (RE'06).

[11]  Martin Manderscheid,et al.  Network Calculus for the Validation of Automotive Ethernet In-vehicle Network Configurations , 2011, 2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery.

[12]  Carlos Parra,et al.  Towards Dynamic Software Product Lines: Unifying Design and Runtime Adaptations , 2011 .

[13]  Antonio Ruiz Cortés,et al.  Mapping Feature Models onto Component Models to Build Dynamic Software Product Lines , 2007, SPLC.

[14]  Dorina C. Petriu,et al.  Automatic Derivation of a Product Performance Model from a Software Product Line Model , 2011, 2011 15th International Software Product Line Conference.

[15]  Jean-Yves Le Boudec,et al.  Network Calculus: A Theory of Deterministic Queuing Systems for the Internet , 2001 .

[16]  Martin Manderscheid,et al.  A formal approach enabling the computation of network state permutations using binary relations , 2011, Proceedings of 2011 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing.