Ensemble Prediction of Job Resources to Improve System Performance for Slurm-Based HPC Systems

Abstract

In this paper, we present a novel methodology for predicting job resources (memory and time) for submitted jobs on HPC systems. Our methodology based on historical jobs data (saccount data) provided from the Slurm workload manager using supervised machine learning. This Machine Learning (ML) prediction model is effective and useful for both HPC administrators and HPC users. Moreover, our ML model increases the efficiency and utilization for HPC systems, thus reduce power consumption as well. Our model involves using Several supervised machine learning discriminative models from the scikit-learn machine learning library and LightGBM applied on historical data from Slurm.

Topics & Concepts