Job Generator for Evaluating and Comparing Scheduling Algorithms for Modern GPU Clouds

Michal Konopa, Jan Fesl, Ladislav Beránek

2025

Abstract

The steep technological and performance advances in GPU cards have led to their increasing use in data centers in the recent years, especially in machine learning jobs. However, high hardware performance alone does not guarantee (sub) optimal utilization of computing resources, especially when the cost associated with power consumption also needs to be increasingly considered. As consequence of these realities, various job scheduling algorithms have been and are being developed to optimize the power consumption in data centers with respect to defined constraints. Unfortunately, there is still no known, widely used, parametrizable dataset that serves as a de facto standard for simulating scheduling algorithms and the resulting ability to compare their performance against each other. The goal of this paper is to describe a simple job set generator designed to run on modern GPU architectures and to introduce the newly created data set suitable for evaluation the scheduling algorithms.

Paper Citation

in Harvard Style

Konopa M., Fesl J. and Beránek L. (2025). Job Generator for Evaluating and Comparing Scheduling Algorithms for Modern GPU Clouds. In Proceedings of the 15th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER; ISBN 978-989-758-747-4, SciTePress, pages 136-143. DOI: 10.5220/0013227500003950

in Bibtex Style

@conference{closer25,
author={Michal Konopa and Jan Fesl and Ladislav Beránek},
title={Job Generator for Evaluating and Comparing Scheduling Algorithms for Modern GPU Clouds},
booktitle={Proceedings of the 15th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER},
year={2025},
pages={136-143},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013227500003950},
isbn={978-989-758-747-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER
TI - Job Generator for Evaluating and Comparing Scheduling Algorithms for Modern GPU Clouds
SN - 978-989-758-747-4
AU - Konopa M.
AU - Fesl J.
AU - Beránek L.
PY - 2025
SP - 136
EP - 143
DO - 10.5220/0013227500003950
PB - SciTePress