  Several scientific and industry applications require High Performance
Computing (HPC) resources to process and/or simulate complex models. Not long
ago, companies, research institutes, and universities used to acquire and
maintain on-premise computer clusters; but, recently, cloud computing has
emerged as an alternative for a subset of HPC applications. This poses a
challenge to end-users, who have to decide where to run their jobs: on local
clusters or burst to a remote cloud service provider. While current research on
HPC cloud has focused on comparing performance of on-premise clusters against
cloud resources, we build on top of existing efforts and introduce an advisory
service to help users make this decision considering the trade-offs of resource
costs, performance, and availability on hybrid clouds. We evaluated our service
using a real test-bed with a seismic processing application based on Full
Waveform Inversion; a technique used by geophysicists in the oil & gas industry
and earthquake prediction. We also discuss how the advisor can be used for
other applications and highlight the main lessons learned constructing this
service to reduce costs and turnaround times.
