  The next generation of High Energy Physics experiments are expected to
generate exabytes of data---two orders of magnitude greater than the current
generation. In order to reliably meet peak demands, facilities must either plan
to provision enough resources to cover the forecasted need, or find ways to
elastically expand their computational capabilities. Commercial cloud and
allocation-based High Performance Computing (HPC) resources both have explicit
and implicit costs that must be considered when deciding when to provision
these resources, and to choose an appropriate scale. In order to support such
provisioning in a manner consistent with organizational business rules and
budget constraints, we have developed a modular intelligent decision support
system (IDSS) to aid in the automatic provisioning of resources---spanning
multiple cloud providers, multiple HPC centers, and grid computing federations.
