  The Monte Carlo (MC) method is the most common technique used for uncertainty
quantification, due to its simplicity and good statistical results. However,
its computational cost is extremely high, and, in many cases, prohibitive.
Fortunately, the MC algorithm is easily parallelizable, which allows its use in
simulations where the computation of a single realization is very costly. This
work presents a methodology for the parallelization of the MC method, in the
context of cloud computing. This strategy is based on the MapReduce paradigm,
and allows an efficient distribution of tasks in the cloud. This methodology is
illustrated on a problem of structural dynamics that is subject to
uncertainties. The results show that the technique is capable of producing good
results concerning statistical moments of low order. It is shown that even a
simple problem may require many realizations for convergence of histograms,
which makes the cloud computing strategy very attractive (due to its high
scalability capacity and low-cost). Additionally, the results regarding the
time of processing and storage space usage allow one to qualify this new
methodology as a solution for simulations that require a number of MC
realizations beyond the standard.
