A robust reliable energy-aware urgent computing resource allocation for flash-flood ensemble forecasting on HPC infrastructures for decision support

Forecasting severe hydro-meteorological events under time constraints requires reliable and robust models, which facilitate energy-aware allocations on high performance computing infrastructures. We present an innovative approach to quantify the performance of six heuristics in selecting optimal allocations to distributed HPC resources for an ensemble of meteorological forecasts for a flash-flood producing storm in Genoa (Liguria, Italy) in October 2014. The computing environments are expected to be dynamic and heterogeneous in nature with varying availability, performance and energy-to-solution. The results of the allocations are assessed and compared. The aim is to provide a robust, reliable and energy-aware resource allocation for ensembles of forecasts for time-critical decision support.