163 research outputs found
Moment Matching-Based Distribution Fitting with Generalized Hyper-Erlang Distributions
This paper describes a novel moment matching based fitting method for phase-type (PH) distributions. A special sub-class of phase-type distributions is introduced for the fitting, called generalized hyper-Erlang distributions. The user has to provide only two parameters: the number of moments to match, and the upper bound for the sum of the multiplicities of the eigenvalues of the distribution, which is related to the maximal size of the resulting PH distribution. Given these two parameters, our method obtains all PH distributions that match the target moments and have a Markovian representation up to the given size. From this set of PH distributions the best one can be selected according to any distance function
A Matrix-Analytic Solution for Randomized Load Balancing Models with Phase-Type Service Times
In this paper, we provide a matrix-analytic solution for randomized load
balancing models (also known as \emph{supermarket models}) with phase-type (PH)
service times. Generalizing the service times to the phase-type distribution
makes the analysis of the supermarket models more difficult and challenging
than that of the exponential service time case which has been extensively
discussed in the literature. We first describe the supermarket model as a
system of differential vector equations, and provide a doubly exponential
solution to the fixed point of the system of differential vector equations.
Then we analyze the exponential convergence of the current location of the
supermarket model to its fixed point. Finally, we present numerical examples to
illustrate our approach and show its effectiveness in analyzing the randomized
load balancing schemes with non-exponential service requirements.Comment: 24 page
A fast EM algorithm for fitting marked Markovian arrival processes with a new special structure
This paper presents an EM algorithm for fitting traces with Markovian arrival processes (MAPs). The proposed algorithm operates on a special subclass of MAPs. This special structure enables the efficient implementation of the EM algorithm; it is more orders of magnitudes faster than methods operating on the general MAP class while providing similar or better likelihood values. An other important feature of the algorithm is that it is able to fit multi-class traces with marked Markovian arrival processes as well. Several numerical examples demonstrate the efficiency of the procedure
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