Predicting the efficiency of job scheduler actions - Kraus, Ana Eloina Nascimento; Diel, Guilerme; Koslovski, Guilherme Piêgas (Anais da Escola Regional de Alto Desempenho da Região Sul (ERAD-RS); 2024: Anais da XXIV Escola Regional de Alto Desempenho da Região SulDO - 10.5753/eradrs.2024.238711, 2024)
The order in which tasks are executed in High Performance Computing (HPC) infrastructures is fundamental to the efficiency of the virtual environment. This article covera a amachine learning- and polynomial regression-aided effort of predicting and thus, drawing out a better understanding of, the individual performances of various job scheduling algorithms.