One of the most common techniques to evaluate the performance of a computer I/O subsystem performance has been found on detailed simulation models including specific features of storage devices like disk geometry, zone splitting, caching, read-ahead buffers and request reordering. However, as soon as a new technological innovation is added, those models need to be reworked to include new devices making difficult to have general models up to date. Another alternative is modeling a storage device as a black-box probabilistic model, where the storage device itself, its interface and the interconnection mechanisms are modeled as a single stochastic process, defining the service time as a random variable with an unknown distribution. This approach allows generating disk service times needing less computational power by means of a variate generator included in a simulator. This approach allows to reach a greater scalability in the I/O subsystems performance evaluation by means of simulation. In this paper, we present a method for building a variate generator from service time experimental data. In order to build the variate generator, both real workloads and synthetic workloads may be used. The workload is used to feed the evaluated disk to obtain service time measurements. From experimental data we build a variate generator that fits the disk service times distribution. We also present a use case of our method, where we have obtained a relative error ranging from 0.45% to 1%.