In search of efficient scheduling heuristics from simulations and Machine Learning

• Lucas Rosa USP
• Alfredo Goldman USP

DOI: https://doi.org/10.5753/wscad_estendido.2022.226323

Resumo

High Performance Computing (HPC) systems are used to solve a number of complex issues in different fields of knowledge. However, these platforms have been rapidly evolving in size and complexity; and ensuring efficiency in managing applications (jobs) has become a challenge. Typically, this management involves scheduling heuristics that consist of functions to order the jobs. In this work we evaluate the limits of regression methods for creating scheduling heuristics. Our results show that the simplest heuristic led to the most efficient scheduling, while the more complex heuristics showed instabilities due to multicollinearity.

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