  Trusting simulation output is crucial for Sandia's mission objectives. We
rely on these simulations to perform our high-consequence mission tasks given
national treaty obligations. Other science and modeling applications, while
they may have high-consequence results, still require the strongest levels of
trust to enable using the result as the foundation for both practical
applications and future research. To this end, the computing community has
developed workflow and provenance systems to aid in both automating simulation
and modeling execution as well as determining exactly how was some output was
created so that conclusions can be drawn from the data.
  Current approaches for workflows and provenance systems are all at the user
level and have little to no system level support making them fragile, difficult
to use, and incomplete solutions. The introduction of container technology is a
first step towards encapsulating and tracking artifacts used in creating data
and resulting insights, but their current implementation is focused solely on
making it easy to deploy an application in an isolated "sandbox" and
maintaining a strictly read-only mode to avoid any potential changes to the
application. All storage activities are still using the system-level shared
storage.
  This project explores extending the container concept to include storage as a
new container type we call \emph{data pallets}. Data Pallets are potentially
writeable, auto generated by the system based on IO activities, and usable as a
way to link the contained data back to the application and input deck used to
create it.
