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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)

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)

The paper introduces a new approach to define process algebras with quantified transitions. A mathematical model is introduced which allows the definition of various classes of process algebras including the well known models of untimed, probabilistic and stochastic process algebras. For this general mathematical model a bisimulation equivalence is defined… (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)

The analysis of stochastic marked graphs is considered. The underlying idea is to decompose the marked graph into subnets, to generate state spaces and transition matrices for these isolated parts and then to represent the generator matrix underlying the complete net by means of much smaller subnet matrices combined via tensor operations. Based on this… (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)

This paper presents an improved algorithm compared to the one given in 7], which nds a minimal deadlock containing a given place p in a strongly connected Free-Choice net (FC-net). Its worst case time complexity is linear in the size of the net. The interest in nding such deadlocks arises from recognising structurally live and bounded FC-nets (LBFC-nets),… (More)

- Peter Kemper, Falko Bause
- 1992

In 3] J. Esparza presented an interesting characterization of structurally live and structurally bounded Free-Choice Nets (LBFC-Nets). Exploiting this characterization in combination with new results and reened algorithms the authors formulate an O(jPjjTjjFj) algorithm deciding whether a Free-Choice Net is a LBFC-Net or not. Furthermore the algorithm… (More)