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Several scientific and industry applications require High PerformanceComputing (HPC) resources to process and/or simulate complex models. Not longago, companies, research institutes, and universities used to acquire andmaintain on-premise computer clusters; but, recently, cloud computing hasemerged as an alternative for a subset of HPC applications. This poses achallenge to end-users, who have to decide where to run their jobs: on localclusters or burst to a remote cloud service provider. While current research onHPC cloud has focused on comparing performance of on-premise clusters againstcloud resources, we build on top of existing efforts and introduce an advisoryservice to help users make this decision considering the trade-offs of resourcecosts, performance, and availability on hybrid clouds. We evaluated our serviceusing a real test-bed with a seismic processing application based on FullWaveform Inversion; a technique used by geophysicists in the oil & gas industryand earthquake prediction. We also discuss how the advisor can be used forother applications and highlight the main lessons learned constructing thisservice to reduce costs and turnaround times.

An SLA-based Advisor for Placement of HPC Jobs on Hybrid Clouds