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 Sul, 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.