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The numerical analysis of various modeling formalisms (4, 10, 141 profits from a structured representation for the generator matrix Q of the underlying continuous time Markov chain, where Q is described by a sum of tensor (kronecker) products of much smaller matrices. In this paper we describe such a representation for the class of superposed generalized(More)
We present new algorithms for the solution of large structured Markov models whose infinitesimal generator can be expressed as a Kronecker expression of sparse matrices. We then compare them with the shuffle-based method commonly used in this context and show how our new algorithms can be advantageous in dealing with very sparse matrices and in supporting(More)
This paper presents a toolset for modelling and analysing logistic networks. The toolset includes a graphical user interface accommodating a " Process Chains " view. It supports model analysis by a variety of methods including simulative, algebraic and numerical techniques. An object-based, hierarchical structure helps to keep track of large models. The(More)
This paper introduces a new approach for the construction of performance models of complex systems integrating software and hardware. Software components are speciied using hierarchical coloured GSPNs which extend the well established coloured GSPNs. Hardware components are composed of basic queues taken from queueing networks. Integration of queues into(More)
In this paper, we describe a novel technique that helps a modeler gain insight into the dynamic behavior of a complex stochastic discrete event simulation model based on trace analysis. We propose algorithms to distinguish progressive from repetitive behavior in a trace and to extract a minimal progressive fragment of a trace. The implied combinatorial(More)
The complexity of stochastic models of real-world systems is usually managed by abstracting details and structuring models in a hierarchical manner. Systems are often built by replicating and joining subsystems, making possible the creation of a model structure that yields lumpable state spaces. This fact has been exploited to facilitate model-based(More)
Markovian arrival processes are a powerful class of stochastic processes to represent stochastic workloads that include autocorrelation in performance or dependability modeling. However, fitting the parameters of a Markovian arrival process to given measurement data is non-trivial and most known methods focus on a single class case, where all events are of(More)