Paper Info
Title
Combining Response Surface Methodology with Numerical Models for Optimization of Class-Based Queueing Systems
Abstract
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 propose the combination of RSM with numerical analysis methods to solve continuous time Markov chain models of class-based queueing systems (CBQ). We consider first- and second-order models in RSM to identify an optimal parameter configuration for CBQ as part of the differentiated service architecture. 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 the proposed optimization methodology we determine a suitable configuration of RSM and compare the results with previous results for optimizing CBQ.
Year
DOI
Venue
2005
10.1109/DSN.2005.28
DSN
Keywords
DocType
ISSN
optimizing CBQ,Combining Response Surface Methodology,optimization technique,Numerical Models,numerical analysis method,numerical solution method,proposed optimization methodology,model-based analysis,Class-Based Queueing Systems,large Markov chain,optimal parameter configuration,response surface methodology,continuous time Markov chain
Conference
1530-0889
ISBN
Citations 
PageRank 
0-7695-2282-3
3
0.46
References 
Authors
9
2
Name
Order
Citations
PageRank
Dennis Muller197.43
Axel Thümmler225723.52