Augmenting ML-based Predictive Modelling with NLP to Forecast a Job's Power Consumption
Francesco Antici, Keiji Yamamoto, Jens Domke, Zeynep Kiziltan
Abstract
As modern High-Performance Computing (HPC) reach exascale performance, their power consumption becomes a serious threat to environmental and energy sustainability. Efficient power management in HPC systems is crucial for optimizing workload management, reducing operational costs, and promoting environmental sustainability. Accurate prediction of job power consumption plays an important role in achieving such goals. In this paper, we apply a technique combining Machine Learning (ML) algorithms with Natural Language Processing (NLP) tools to predict job power consumption. The solution is able to predict job maximum and average power consumption per node, leveraging only information which is available at the time of job submission. The prediction is performed in an online fashion, and we validate the approach using batch system logs extracted from Supercomputer Fugaku, hosted at the RIKEN Center for Computational Science, in Japan. The experimental evaluation shows promising results of outperforming classical technique while obtaining an R2 score of more than 0.53 for our two prediction tasks.