  A common task in scientific computing is the derivation of data. This
workflow extracts the most important information from large input data and
stores it in smaller derived data objects. The derived data objects can then be
used for further analysis tasks.
  Typically, those workflows use distributed storage and computing resources. A
straightforward configuration of storage media would be low cost tape storage
and higher cost disk storage. The large, infrequently accessed input data is
stored on tape storage. The smaller, frequently accessed derived data is stored
on disk storage. In a best case scenario, the large input data is only accessed
very infrequently and in a well planned pattern. However, practice shows that
often the data has to be processed continuously and unpredictably. This can
significantly reduce tape storage performance. A common approach to counter
this is storing copies of the large input data on disk storage.
  This contribution evaluates an approach that uses cloud storage resources to
serve as a flexible cache or buffer depending on the computational workflow.
The proposed model is elaborated for the case of continuously processed data.
For the evaluation, a simulation was developed, which can be used to evaluate
models related to storage and network resources.
  We show that using commercial cloud storage can reduce the on-premises disk
storage requirements, while maintaining an equal throughput of jobs. Moreover,
the key metrics of the model are discussed and an approach is described that
uses the simulation to assist with the decision process of using commercial
cloud storage. The goal is to investigate approaches and propose new evaluation
methods to overcome the future data challenges.
