With the cloud paradigm and the concept of everything as a service (XasS), our ability to leverage the potential of distributed computing resources seems greater than ever. On the other hand, data farming is a methodology based on the idea that by repeatedly running a simulation model on a vast parameter space, enough output data can be gathered to provide an meaningful insight into relations between the model's properties and its behaviours, with respect to the simulation's input parameters. In this paper, we present an extension of a data farming computing platform, named Scalarm, and it's evaluation in the context of molecular dynamics (MD) simulations on heterogeneous resources, such as clusters and cloud systems. As a case study, this paper demonstrates how MD simulations can be run with Scalarm on different infrastructures easily without requiring any modifications to the source code of the original MD simulation program. Finally, results from nano droplet simulation runs are presented, that show the advantages of the Scalarm platform for running MD simulations on a heterogeneous infrastructure -- not only for collecting pure numeric data, but also for automated post processing and visualization of the results.