doi:10.1016/j.peva.2003.07.003
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,
,
, A. Horváth
, b and M. Telek
, 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
Fig. 1. Canonical representation of acyclic CPH distributions and its constraints.
Fig. 2. Canonical representation of acyclic DPH distributions and its constraints.
Fig. 5. DPH representation of the discrete uniform distribution [
a=2,
b=6].
Fig. 6. Approximating the L3 distribution with 10-phase PH approximations.
Fig. 7. Distance measure as the function of the scale factor δ for low cv
2 (L3).
Fig. 8. Distance measure as the function of the scale factor δ for high cv
2 (L1).
Fig. 9. Distance measure as the function of the scale factor δ for Uniform(1, 2) (U2).
Fig. 10. Distance measure as the function of the scale factor δ for Uniform(0, 1) (U1).
Fig. 11. Approximating the Uniform(0, 1) distribution (U1).
Fig. 12. Distance measure as the function of the scale factor for the Exponential(1.0) distribution.
Fig. 13. The state space of the considered M/G/1/2/2 queue.
Fig. 14. ε
SUM for distribution L3 with 1-phase approximation for exponential durations.
Fig. 15. ε
SUM for distribution L3 with 2-phase approximation for exponential durations.
Fig. 16. ε
MAX for distribution L3 with 1-phase approximation for exponential durations.
Fig. 17. ε
MAX for distribution L3 with 2-phase approximation for exponential durations.
Fig. 18. ε
SUM for distribution L1 with 1-phase approximation for exponential durations.
Fig. 19. ε
SUM for distribution L1 with 2-phase approximation for exponential durations.
Fig. 20. ε
SUM for distribution U1 with 1-phase approximation for exponential durations.
Fig. 21. ε
SUM for distribution U1 with 2-phase approximation for exponential durations.
Fig. 22. ε
SUM for distribution U2 with 1-phase approximation for exponential durations.
Fig. 23. ε
SUM for distribution U2 with 2-phase approximation for exponential durations.
Fig. 24. ε
SUM for service times L3–L3 with 1-phase approximation for exponential durations.
Fig. 25. ε
SUM for service times L1–L3 with 1-phase approximation for exponential durations.
Fig. 26. Transient probabilities starting from
s1.
Fig. 27. Transient probabilities starting from
s4.
Table 1. Upper and lower bound of δ for fitting distribution L3

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