A Comparison of Task Schedulers Based on Machine Learning

• Claudinei Cabral Junior UDESC
• Guilherme Piêgas Koslovski UDESC

DOI: https://doi.org/10.5753/eradrs.2025.6816

Abstract

Task scheduling plays a key role in computational systems, directly impacting efficiency and resource utilization. Traditional algorithms face difficulties in dynamic environments, making machine learning-based approaches a promising alternative. Among the existing approaches, this work compares the Decima and ACRL schedulers, which are based on Reinforcement Learning.

Keywords: Machine Learning and High Performance Computing, Scheduling and Load Balancing

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