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

Leong, Siew Hoon; Parodi, Antonio and Kranzlmüller, Dieter (2017): A robust reliable energy-aware urgent computing resource allocation for flash-flood ensemble forecasting on HPC infrastructures for decision support. In: Future Generation Computer Systems-the international Journal of Escience, Vol. 68: pp. 136-149

Abstract

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.

• Item Type: Journal article
• Faculties: Mathematics, Computer Science and Statistics > Computer Science
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• ISSN: 0167-739X
• Language: English