Open AccessSimulation of Generalised Semi-Markov Processes based on Graph Transformation Systems
Author(s) -
Piotr Kosiuczenko,
Georgios Lajios
Publication year - 2007
Publication title -
electronic notes in theoretical computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.242
H-Index - 60
ISSN - 1571-0661
DOI - 10.1016/j.entcs.2007.04.018
Subject(s) - computer science , graph rewriting , model transformation , transformation (genetics) , theoretical computer science , control reconfiguration , concurrency , graph , mathematical optimization , exponential random graph models , exponential function , mathematics , distributed computing , random graph , artificial intelligence , biochemistry , chemistry , mathematical analysis , consistency (knowledge bases) , gene , embedded system
tochastic Graph Transformation combines graphical modelling of various software artefacts with stochastic analysis techniques. Existing approaches are restricted to processes with exponential time distribution. Such processes are sufficient for modelling a significant class of stochastic systems, however there are interesting systems which cannot be specified appropriately in such a framework. In several cases one needs to consider non-exponential time distributions. This paper proposes a stochastic model based on graph transformation with general probability distributions. This model is well suited to represent concurrency and performance aspects of architecture reconfiguration. It is also possible to apply Monte Carlo simulation techniques in order to analyse behaviour of complex stochastic systems. The new model is implemented and used to simulate simple networks
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