TY - JOUR

T1 - Tool Support for Performance Modeling and Optimization

AU - Syrjakow, Michael

AU - Syrjakow, Elisabeth

AU - Szczerbicki, Helena

N1 - We want to thank Prof. D. Schmid for his encouragement and support of our work. We also thank our students, especially S. Schillinger, F. Schmidt, H. Renfranz, T. Sommer, D. Haag, C. Bentz, J. Gramlich, and A. Kehl, for their engagement and contributions.

PY - 2006/1

Y1 - 2006/1

N2 - Most of the available modeling and simulation tools for performance analysis do not support model optimization sufficiently. One reason for this unsatisfactory situation is the lack of universally applicable and adaptive optimization strategies. Another reason is that modeling and simulation tools usually have a monolithic software design, which is difficult to extend with experimentation functionality. Such functionality has gained on importance in recent years due to the capability of an automatic extraction of valuable information and knowledge out of complex models. One of the most important experimentation goals is to find model parameter settings, which produce optimal model behavior. In this paper, we elaborate on the design of a powerful optimization component and its integration into existing modeling and simulation tools. For that purpose, we propose a hybrid integration approach being a combination of loose document-based and tight invocation-based integration concepts. Beside the integration concept for the optimization component, we also give a detailed insight into the applied optimization strategies.

AB - Most of the available modeling and simulation tools for performance analysis do not support model optimization sufficiently. One reason for this unsatisfactory situation is the lack of universally applicable and adaptive optimization strategies. Another reason is that modeling and simulation tools usually have a monolithic software design, which is difficult to extend with experimentation functionality. Such functionality has gained on importance in recent years due to the capability of an automatic extraction of valuable information and knowledge out of complex models. One of the most important experimentation goals is to find model parameter settings, which produce optimal model behavior. In this paper, we elaborate on the design of a powerful optimization component and its integration into existing modeling and simulation tools. For that purpose, we propose a hybrid integration approach being a combination of loose document-based and tight invocation-based integration concepts. Beside the integration concept for the optimization component, we also give a detailed insight into the applied optimization strategies.

KW - knowledge and information management

KW - model optimization

KW - performance modeling

KW - Petri Nets

UR - http://www.scopus.com/inward/record.url?scp=85001839729&partnerID=8YFLogxK

U2 - 10.4018/jeis.2006010103

DO - 10.4018/jeis.2006010103

M3 - Article

AN - SCOPUS:85001839729

VL - 2

SP - 30

EP - 53

JO - International Journal of Enterprise Information Systems (IJEIS)

JF - International Journal of Enterprise Information Systems (IJEIS)

SN - 1548-1115

IS - 1

ER -