Аннотация:Empirical studies have repeatedly shownthat in High-Performance Computing HPC users’resource estimations lackaccuracy. Therefore, resource underestimation may remove the job at any step of computing and subsequently allocated resources will be wasted. Moreover, resource overestimation also will waste resources. In this work, to effectively utilize the overall HPC system, we proposed a new approach to predict the required resources such as; number of required CPUs, time slots etc. for newly submitted job. The study focused on predictive analyticstasksincluding regression and classification. Asupervised machine learning system, comprising severalmodels,was trained based on the collection of statistical data including per-job and per-user features collected from the reference queue systems. Results indicated that adding more features to the dataset improves the prediction accuracy. The possibility of designinga plugin to apply our machine learning system in practical applications was studied.A dynamically connected SLURM SPANK plugin was createdthat adds the “--predict-time” option and takes control on srun and sbatch commandswhile they are executed. It was found that the plugin enablespractical use of ourproposed machine learning system.