Performance Evaluation - Special issue: 6th international conference on modelling techniques and tools for computer performance evaluation
A Modeling Framework to Implement Preemption Policies in Non-Markovian SPNs
IEEE Transactions on Software Engineering
Performance Analysis of Communication Systems with Non-Markovian Stochastic Petri Nets
Performance Analysis of Communication Systems with Non-Markovian Stochastic Petri Nets
A Characterization of the Stochastic Process Underlying a Stochastic Petri Net
IEEE Transactions on Software Engineering
Time Domain Analysis of Non-Markovian Stochastic Petri Nets with PRI Transitions
IEEE Transactions on Software Engineering
International Workshop on Timed Petri Nets
PhFit: A General Phase-Type Fitting Tool
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
On Phased Delay Stochastic Petri Nets: Definition and an Application
PNPM '01 Proceedings of the 9th international Workshop on Petri Nets and Performance Models (PNPM'01)
Acyclic discrete phase type distributions: properties and a parameter estimation algorithm
Performance Evaluation
Design and evaluation of web proxies by leveraging self-similarity of web traffic
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Network modelling and simulation
A general model for long-tailed network traffic approximation
The Journal of Supercomputing
Approximate transient analysis of large stochastic models with WinPEPSY-QNS
Computer Networks: The International Journal of Computer and Telecommunications Networking
On moments of discrete phase-type distributions
EPEW'05/WS-FM'05 Proceedings of the 2005 international conference on European Performance Engineering, and Web Services and Formal Methods, international conference on Formal Techniques for Computer Systems and Business Processes
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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.