research-article

Calibration of deployment simulation models: a multi-paradigm modelling approach

Authors:

Hans Vangheluwe,

Paul De Meulenaere,

Serge DemeyerAuthors Info & Claims

TMS/DEVS '12: Proceedings of the 2012 Symposium on Theory of Modeling and Simulation - DEVS Integrative M&S Symposium

Article No.: 13, Pages 1 - 8

Published: 26 March 2012 Publication History

Abstract

In embedded systems development, software engineers increasingly rely on modelling and simulation to produce optimal design solutions. A bottleneck in the Modelling and Simulation Based Design (MSBD) process is model calibration. Setting up experiments to estimate parameter values such that the model accurately reflects real-world system structure and behaviour is technically complex and labour intensive. Parameters to be estimated are for example effective processor speed, memory consumption and network throughput of the hardware platform on which software is deployed. In this paper we show how Multi-Paradigm Modelling (MPM) allows for the synthesis of a model calibration infrastructure. This includes the synthesis, from a model, of a simulator for the "environment" in which a system-to-be built will operate. To demonstrate the feasibility of our approach, we calibrate the model of an automotive power window controller running on the AUTOSAR-platform.

References

[1]

K. Keutzer, A. R. Newton, J. M. Rabaey, and A. Sangiovanni-Vincentelli. System-level design: Orthogonalization of concerns and platform-based design. Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on, 19(12):1523--1543, 2000.

Digital Library

[2]

F. Balarin, Y. Watanabe, H. Hsieh, L. Lavagno, C. Passerone, and a. Sangiovanni-Vincentelli. Metropolis: an integrated electronic system design environment. Computer, 36(4):45--52, April 2003.

Digital Library

[3]

Andy D. Pimentel. The Artemis workbench for system-level performance evaluation of embedded systems. International Journal of Embedded Systems, 3(3):181, 2008.

[4]

S. Becker, H. Koziolek, and R. Reussner. The Palladio component model for model-driven performance prediction. Journal of Systems and Software, 82(1):3--22, January 2009.

Digital Library

[5]

Joachim Denil, Hans Vangheluwe, Pieter Ramaekers, et al. DEVS for AUTOSAR platform modelling. In Proceedings of 2011 Spring Simulation Conference (SpringSim11), DEVS Symposium. New York, NY, 2011.

Digital Library

[6]

Prih Hastono, Stephan Klaus, and S. A. Huss. An integrated SystemC framework for real-time scheduling assessments on system level. In Proceedings of IEEE Int. Real-Time Systems Symposium. Citeseer, 2004.

[7]

Andy D. Pimentel, Mark Thompson, Simon Polstra, and Cagkan Erbas. Calibration of Abstract Performance Models for System-Level Design Space Exploration. Journal of Signal Processing Systems, 50(2):99--114, June 2007.

Digital Library

[8]

Pieter J. Mosterman and Hans Vangheluwe. Computer automated multi-paradigm modeling: An introduction. Simulation, 80(9):433, September 2004.

[9]

David Harel. Statecharts: A visual formalism for complex systems. Science of computer programming, 8(3):231--274, 1987.

Digital Library

[10]

Juan De Lara and Hans Vangheluwe. AToM³: A tool for multi-formalism and meta-modelling. Fundamental approaches to software engineering, pages 174--188, 2002.

Digital Library

[11]

Hans Vangheluwe and Juan De Lara. Multi-Paradigm Modelling and Simulation. In AI, Simulation and Planning in High Autonomy Systems, 2002.

[12]

Christian Ferdinand. Worst case execution time prediction by static program analysis. In Proceedings of the 18th International Parallel and Distributed Processing Symposium, volume 00, pages 17--19. IEEE Computer Society, 2004.

[13]

Jakob Engblom, Andreas Ermedahl, Mikael Sjodin, Jan Gustafsson, and Hans Hansson. Worst-case execution-time analysis for embedded real-time systems. International Journal on Software Tools for Technology Transfer (STTT), 4(4):437--455, August 2003.

[14]

Guillem Bernat, Antoine Colin, and Stefan Petters. pWCET: A tool for probabilistic worst-case execution time analysis of real-time systems. Technical report, 2003.

[15]

Raimund Kirner, Roland Lang, and Gerald Freiberger. Fully automatic worst-case execution time analysis for Matlab/Simulink models. In 14th EUROMICRO Conference on Real-Time Systems, 2002.

Digital Library

[16]

Reinhard Wilhelm, Tulika Mitra, Frank Mueller, Isabelle Puaut, Peter Puschner, Jan Staschulat, Per Stenström, Jakob Engblom, Andreas Ermedahl, Niklas Holsti, Stephan Thesing, David Whalley, Guillem Bernat, Christian Ferdinand, and Reinhold Heckmann. The worst-case execution-time problem: overview of methods and survey of tools. ACM Transactions on Embedded Computing Systems, 7(3):1--53, April 2008.

Digital Library

[17]

Sebastian Fischmeister and Patrick Lam. Time-aware Instrumentation of Embedded Software. Industrial Informatics, IEEE Transactions on, 6(4):652--663, 2010.

[18]

Markus Debusmann and Kurt Geihs. Efficient and transparent instrumentation of application components using an aspect-oriented approach. Self-Managing Distributed Systems, pages 227--240, 2003.

[19]

Vahid Garousi, L. C. Briand, and Yvan Labiche. Traffic-aware stress testing of distributed systems based on UML models. In Proceedings of the 28th international conference on Software engineering, pages 391--400. ACM, 2006.

Digital Library

[20]

H. Hanselmann. Hardware-in-the-loop simulation testing and its integration into a cacsd toolset. In Computer-Aided Control System Design, 1996., Proceedings of the 1996 IEEE International Symposium on, pages 152--156, sep 1996.

Cited By

Denil JKashif HArafa PVangheluwe HFischmeister S(2015)Instrumentation and preservation of extra-functional properties of simulink modelsProceedings of the Symposium on Theory of Modeling & Simulation: DEVS Integrative M&S Symposium10.5555/2872965.2872972(47-54)Online publication date: 12-Apr-2015
https://dl.acm.org/doi/10.5555/2872965.2872972

Calibration of deployment simulation models: a multi-paradigm modelling approach
1. Computer systems organization

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Published In

cover image ACM Other conferences

TMS/DEVS '12: Proceedings of the 2012 Symposium on Theory of Modeling and Simulation - DEVS Integrative M&S Symposium

March 2012

394 pages

ISBN:9781618397867

General Chairs:
Gabriel Wainer
Carleton University
,
Pieter Mosterman
MathWorks

Sponsors

SCS: Society for Modeling and Simulation International

In-Cooperation

SIGSIM: ACM Special Interest Group on Simulation and Modeling

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Society for Computer Simulation International

San Diego, CA, United States

Publication History

Published: 26 March 2012

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SpringSim '12

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SpringSim '12: 2012 Spring Simulation Multiconference

March 26 - 30, 2012

Florida, Orlando

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Cited By

Denil JKashif HArafa PVangheluwe HFischmeister S(2015)Instrumentation and preservation of extra-functional properties of simulink modelsProceedings of the Symposium on Theory of Modeling & Simulation: DEVS Integrative M&S Symposium10.5555/2872965.2872972(47-54)Online publication date: 12-Apr-2015
https://dl.acm.org/doi/10.5555/2872965.2872972

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