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

Job Placement Advisor Based on Turnaround Predictions for HPC Hybrid Clouds