How to integrate inter-component dependencies into combinatorial availability models

In this paper, a novel modeling method for highly available systems is proposed. As an input, the model accepts common reliability block diagrams, which are widely used because of their excellent manageability. However, unlike traditional solution methods for block diagrams, the proposed method also supports the attribution of the model with several kinds of inter-component dependencies. Thus, the evaluation of such a model yields much more realistic results, similar to using state-based models like Markovian chains (MC) or generalized stochastic Petri nets (GSPN. [M. Ajmone Marson et al., 1995, R.A. Saner et al., 1996]). However, compared to traditional state-based models, the proposed method offers a much better manageability. This means that all models are intuitive, clear, and can easily be modified, as well as created and refined in a stepwise manner. These advantages are exemplified by a realistic industrial application from the area of telecommunications. As the proposed models cannot be solved with classical solution methods for combinatorial availability models, we propose a new evaluation technique which is based on a transformation of the input models into semantically equivalent state-based models. This solution technique was implemented in the software tool OpenSESAME (simple but extensive structured availability modeling environment).

1. Introduction

Modeling tools and methods for availability evaluation can be categorized by their underlying mathematical model into combinatorial (or Boolean) methods (e.g. [12], [13]) and state-based methods (e.g. [1], [10], [15]). Fault Trees and Reliability Block Diagrams are prominent examples for the first class of methods, whereas tools based on Markovian Chains or (Generalized) Stochastic Petri Nets can be assigned to the second class.