<p>Scheduling jobs for executing atop large-scale High-Performance Computing (HPC) infrastructures is a fundamental administrative challenge essential to maximize the productivity indicators of such complex Data Centers (DCs). Several policies were proposed by specialized literature, considering specific scenarios and applications, or proposing DC-tailored heuristics. However, the definition of the appropriate policy depends directly on the applications and infrastructure load, that is, indicators that vary over time. Given this context, this work advances the field on applying Machine Learning (ML) techniques to improve the existing scheduling policies. Specifically, we propose Knowledge-based Job Scheduler (KJS), which uses regression techniques to characterize the performance indicators of existing schedulers, composing a knowledge database, and instead of proposing a one-size-fits-all scheduling function, KJS consolidates the information and uses data classification for defining the jobs’ execution order. The simulation campaign demonstrates that KJS can adapt to different workloads when compared to the individual use of existing policies. Essentially, KJS combines polynomial data regression and classification for improving the performance indicators of HPC DC, in both the users’ and the administrators’ perspectives.</p>