On limits of Machine Learning techniques in the learning of scheduling policies

This scientific initiation work explores the emerging relationship between managing resources on high-performance computing (HPC) platforms and the use of regression-derived scheduling heuristics to optimize performance. Recent research has shown that machine learning (ML) techniques can be used to generate scheduling heuristics that are simple and efficient. This work proposes an alternative approach using polynomial functions to generate scheduling heuristics. The simplest polynomial was found to be one of the most efficient heuristic. We also evaluated the resilience of the regression-derived heuristics over time. We published two papers in peer-reviewed national and international workshops (Qualis-B3/B4).