Markovian arrival processes are a powerful class of stochastic processes to represent
stochastic workloads that include autocorrelation in performance or dependability modeling …

Manufacturing has to cope with the continuously increasing variety of products, change of
volumes and shortening product life cycles. These trends also affect the automotive sector …

In this paper we investigate the flexibility of matrix distributions for the modeling of mortality.
Starting from a simple Gompertz law, we show how the introduction of matrix-valued …

We apply supervised learning to a general problem in queueing theory: using a neural net,
we develop a fast and accurate predictor of the stationary system-length distribution of a …

Fitting of the parameters of a Phase Type (PH) distribution or a Markovian Arrival Process
(MAP) according to some quantities of measured data streams is still a challenge. This …

We present a new method for the accurate analysis of the quality-of-service (QoS) properties
of component-based systems. Our method takes as input a QoS property of interest and a …

Abstract Analysis of stochastic models is often hurdled by the complexity of probability
density functions associated with generally distributed random variables. Among the …

The stability of a cyclic polling system, with a single server and two infinite-buffer queues, is
considered. Customers arrive at the two queues according to independent batch Markovian …

Abstract Phase Type Distributions (PHDs) and Markovian Arrival Processes (MAPs) are
versatile models for the modeling of timing behavior in stochastic models. The …

Phase-type distributions, the probability distributions generated by the time to absorption in
a continuous-time Markov chain, are a popular tool for modeling time-dependent system …