Introduction

The core of MOSEL consists of a set of language constructs to specify the possible states and state changes of a system. Additional syntax is dedicated to the description of the conditions under which transitions are allowed or prohibited. In order to model the temporal behaviour of the system adequately, possible state changes can either occur immediately or after a delay which is chosen according to a continuous probability distribution. Not only exponentially distributed delays are possible, the delay can also be distributed according to a Pareto- , Weibull-, Normal- and some more kinds of distributions. If a transition will always occur after a fixed amount of time (e.g. timeouts in protocols) a deterministic transition can be specified in the MOSEL model in oder to reflect this kind of system behaviour appropriately. Moreover, transitions which occur during a time interval with known start- and endpoints and equal probability for each timepoint in the interval can be modelled as a uniformly distributed transition in MOSEL.

In contrast to many specification languages of existing performance and reliability modeling and analysis tools, which often tend to be too verbose, most MOSEL specifications are compact but anyhow easy to understand. MOSEL contains many keywords and control structures which are also used in general purpose programming languages. This creates a familiar ambiance for many practitioners.

Moreover, MOSEL provides means by which many interesting performance or reliability measures and the graphical presentation of them can be specified straightforward. Reward-like result specifications are possible. It is especially easy to analyse a model with many sets of different system parameters.

The benefit of MOSEL - especially for the practitioner from the industry - lies in its modeling environment: A MOSEL model can automatically be translated into various tool-specific system descriptions and then are analyzed by the appropriate tools. This exempts the modeller from the time-consuming task of learning different modeling languages.