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Combining Response Surface Methodology with Numerical Methods for Optimization of Markovian Models
July-September 2006 (vol. 3 no. 3)
pp. 259-269
ASCII Text
x 
 
Peter Kemper, Dennis M?, Axel Th?mmler, "Combining Response Surface Methodology with Numerical Methods for Optimization of Markovian Models," IEEE Transactions on Dependable and Secure Computing, vol. 3, no. 3, pp. 259-269, July-September, 2006.
 
 
BibTex
x
 
@article{ 10.1109/TDSC.2006.28,
author = {Peter Kemper and Dennis M? and Axel Th?mmler},
title = {Combining Response Surface Methodology with Numerical Methods for Optimization of Markovian Models},
journal ={IEEE Transactions on Dependable and Secure Computing},
volume = {3},
number = {3},
issn = {1545-5971},
year = {2006},
pages = {259-269},
doi = {http://doi.ieeecomputersociety.org/10.1109/TDSC.2006.28},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Dependable and Secure Computing
TI - Combining Response Surface Methodology with Numerical Methods for Optimization of Markovian Models
IS - 3
SN - 1545-5971
SP259
EP269
EPD - 259-269
A1 - Peter Kemper,
A1 - Dennis M?,
A1 - Axel Th?mmler,
PY - 2006
KW - Constrained optimization
KW - Markov processes
KW - sparse
KW - structured
KW - and very large systems
KW - performance analysis and design aids
KW - communication networks.
VL - 3
JA - IEEE Transactions on Dependable and Secure Computing
ER -
 
In general, decision support is one of the main purposes of model-based analysis of systems. Response surface methodology (RSM) is an optimization technique that has been applied frequently in practice, but few automated variants are currently available. In this paper, we show how to combine RSM with numerical analysis methods to optimize continuous time Markov chain models. Among the many known numerical solution methods for large Markov chains, we consider a Gauss-Seidel solver with relaxation that relies on a hierarchical Kronecker representation as implemented in the APNN Toolbox. To effectively apply RSM for optimizing numerical models, we propose three strategies which are shown to reduce the required number of iterations of the numerical solver. With a set of experiments, we evaluate the proposed strategies with a model of a production line and apply them to optimize a class-based queueing system.

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Index Terms:
Constrained optimization, Markov processes, sparse, structured, and very large systems, performance analysis and design aids, communication networks.
Citation:
Peter Kemper, Dennis M?, Axel Th?mmler, "Combining Response Surface Methodology with Numerical Methods for Optimization of Markovian Models," IEEE Transactions on Dependable and Secure Computing, vol. 3, no. 3, pp. 259-269, July-Sept. 2006, doi:10.1109/TDSC.2006.28
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