Using GoldSim's Probabilistic Simulation Engine

Once a model of the Computer we discussed in the previous topics has been constructed, we can simulate the system to predict how it will perform through time. By definition, however, the performance of such a system is stochastic (i.e., inherently variable). That is, we can't say exactly when a component will fail; we can only describe the failure (and repair) process statistically. For example, if we had 100 identical computers, their failure (and repair) histories would not be identical. They would display a distribution of behaviors.

In addition to this inherent variability, we might also be uncertain about some of the input parameters controlling the model. For example, if we had not carried out actual tests on the components, the parameters describing their failure modes would be uncertain, and we could enter these as probability distributions in order to capture this uncertainty.

Variability and uncertainty are represented in GoldSim using Monte Carlo simulation A method for propagating (translating) uncertainties in model inputs into uncertainties in model results.. Monte Carlo simulation consists of a calculating a large number of "realizations" (potential futures). In our computer example, this would be equivalent to simulating the behavior of a large number of computers through time.

After a simulation has been run, GoldSim provides a number of reliability analysis tools that become available at the end of a simulation. These are discussed in the next topic.