A basic type of simulation known as Monte Carlo simulation is used to overcome the difficulties associated with analyzing systems with complex dependencies. To compute results with more efficiency, various improvements to the simulation process can be implemented, including variance reduction techniques such as importance sampling and special types of simulation methods for computing steady state results. Such advanced methods are used to improve the simulation process when applicable.

Simulation is based on the statistical concept that when the number of trials of an experiment approaches infinity, the average of the experiment's outcome is equivalent to the expected value of the random variable under study. When availability is computed, the outcome is either system success or failure. The average of this outcome is the expected portion of success states, which is the system availability.

In reality, performing an infinite number of simulations is impossible. Therefore, an appropriate number of iterations must be specified. Determining the number of simulation iterations that produce results conforming to the required accuracy within a reasonable computational time is important. As described in Simulation Iterations, this value varies, depending on several parameters.