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Performance Evaluation
Volume 56, Issues 1-4, March 2004, Pages 121-144
Dependable Systems and Networks - Performance and Dependability Symposium (DSN-PDS) 2002: Selected Papers
 
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doi:10.1016/j.peva.2003.07.003    
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Copyright © 2003 Elsevier B.V. All rights reserved.

The scale factor: a new degree of freedom in phase-type approximation

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A. Bobbioa, Corresponding Author Contact Information, E-mail The Corresponding Author, A. HorváthE-mail The Corresponding Author, b and M. TelekE-mail The Corresponding Author, c

a Dipartimento di Scienze e Tecnologie Avanzate, Università del Piemonte Orientale, Alessandria, Italy

b Dipartimento di Informatica, Università di Torino, Turin, Italy

c Department of Telecommunications, Technical University of Budapest, Budapest, Hungary


Available online 30 September 2003.

Abstract

This paper introduces a unified approach to phase-type approximation in which the discrete and the continuous phase-type models form a common model set. The models of this common set are assigned with a non-negative real parameter, the scale factor. The case when the scale factor is strictly positive results in discrete phase-type distributions and the scale factor represents the time elapsed in one step. If the scale factor is 0, the resulting class is the class of CPH distributions. Applying the above view, it is shown that there is no qualitative difference between the discrete and the CPH models. Based on this unified view of phase-type models one can choose the best phase-type approximation of a stochastic model by optimizing the scale factor.

Author Keywords: Discrete and continuous phase-type distributions; Phase-type expansion; Approximate analysis

Article Outline

1. Introduction
2. Definition and notation
3. Comparing properties of CPH and DPH distributions
3.1. First-order discrete approximation of CTMCs
3.2. The minimum coefficient of variation
3.3. The minimum coefficient of variation of scaled DPH distributions
3.4. DPH distributions with finite support
4. The optimal δ in PH fitting
4.1. Fitting distributions with low cv2
4.2. Fitting distributions with high cv2
4.3. Fitting distributions with finite support
4.4. Approximating exponential distributions
5. Approximating non-Markovian models
5.1. Steady-state behavior
5.2. Transient behavior
6. Concluding remarks
6.1. Advantages of using DPH
6.2. Disadvantages of using DPH
Acknowledgements
References
Vitae



























Corresponding Author Contact InformationCorresponding author.


Performance Evaluation
Volume 56, Issues 1-4, March 2004, Pages 121-144
Dependable Systems and Networks - Performance and Dependability Symposium (DSN-PDS) 2002: Selected Papers
 
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