Aggregation Methods for Markov Reward Chains with Fast and Silent Transitions
Conference: MMB 2008 - 14th GI/ITG Conference - Measurement, Modelling and Evalutation of Computer and Communication Systems
03/31/2008 - 04/02/2008 at Dortmund, Germany
Proceedings: MMB 2008
Pages: 15Language: englishTyp: PDFPersonal VDE Members are entitled to a 10% discount on this title
Markovski, Jasen; Treka, Nikola (Department of Mathematics and Computer Science, Eindhoven University of Technology, P.O. Box 513, NL-5600 MB Eindhoven, The Netherlands)
We develop and compare reduction- and lumping-based aggregation methods for continuous-time Markov reward chains with different types of instantaneous transitions. Such models arise from the intermediate performance models of many high-level modeling formalisms like, for example, stochastic process algebras or (generalized) stochastic Petri nets. In our setting the instantaneous transitions are modeled as transitions parameterized with a real variable. They are called fast transitions when governed by explicit probabilities, and silent transitions when the probabilities are left unspecified. When the parameter tends to infinity the transitions introduce stochastic discontinuity in the model. We study the proposed two aggregation methods in three different settings of Markovian models with stochastic discontinuity, fast transitions, and silent transitions, respectively, and show that the methods coincide when all instantaneous transitions are eliminated.