  Several companies and research institutes are moving their CPU-intensive
applications to hybrid High Performance Computing (HPC) cloud environments.
Such a shift depends on the creation of software systems that help users decide
where a job should be placed considering execution time and queue wait time to
access on-premise clusters. Relying blindly on turnaround prediction techniques
will affect negatively response times inside HPC cloud environments. This paper
introduces a tool to make job placement decisions in HPC hybrid cloud
environments taking into account the inaccuracy of execution and waiting time
predictions. We used job traces from real supercomputing centers to run our
experiments, and compared the performance between environments using real
speedup curves. We also extended a state-of-the-art machine learning based
predictor to work with data from the cluster scheduler. Our main findings are:
(i) depending on workload characteristics, there is a turning point where
predictions should be disregarded in favor of a more conservative decision to
minimize job turnaround times and (ii) scheduler data plays a key role in
improving predictions generated with machine learning using job trace
data---our experiments showed around 20% prediction accuracy improvements.
