Mitigating Concept Drift in Job Execution Time Prediction Models - Gallo, Bernardo; Drummond, Lúcia; Viterbo, José (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), 2025)
This work investigates the mitigation of Concept Drift in machine learning models applied to job execution time prediction in High Performance Computing (HPC) systems. The study assesses the impact of a periodic evaluation strategy with retraining in a machine learning model using SLURM logs from a Petrobras supercomputer. Our experiments demonstrate that Concept Drift significantly impacts the reliability of machine learning models over time. We show that a strategy of daily model evaluation and retraining, triggered when the Mean Absolute Percentage Error (MAPE) exceeds 150%, effectively mitigates Concept Drift. This approach significantly reduces the average MAPE in different periods. The results underscore the necessity of daily evaluations to maintain acceptable predictive model performance in such dynamic settings. This study highlights a practical solution for mitigating Concept Drift, thereby enhancing the applicability of ML models in HPC systems and showcasing their potential to optimize processes in critical sectors like oil and gas.