Linux Configuration Tuning: Is Having a Large Dataset Enough?

Hifza Khalid; Peter Portante; Alva Couch

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Paper

Linux Configuration Tuning: Is Having a Large Dataset Enough?

Topics: Data Mining and Algorithms for Big Data

In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods ICPRAM - Volume 1, 771-778, 2024 , Rome, Italy

Authors: Hifza Khalid ¹ ; Peter Portante ² and Alva Couch ¹

Affiliations: ¹ Department of Computer Science, Tufts University, MA, U.S.A. ; ² Red Hat Inc., 100 East Davie Street, Raleigh, NC, U.S.A.

Keyword(s): Linux, Configuration Tuning, Network Performance, Feature Selection, Data Diversity.

Abstract: While it would seem that enough data can solve any problem, data quality determines the appropriateness of data to solve specific problems. We intended to use a large dataset of performance data for the Linux operating system to suggest optimal tuning for network applications. We conducted a series of experiments to select hardware and Linux configuration options that are significant to network performance. Our results showed that network performance was mainly a function of workload and hardware. Investigating these results showed that our dataset did not contain enough diversity in configuration settings to infer the best tuning and was only useful for making hardware recommendations. Others with similar problems can use our tests to save time in concluding that a particular dataset is not suitable for machine learning.

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Paper citation in several formats:

Khalid, H., Portante, P. and Couch, A. (2024). Linux Configuration Tuning: Is Having a Large Dataset Enough?. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-684-2; ISSN 2184-4313, SciTePress, pages 771-778. DOI: 10.5220/0012387200003654

@conference{icpram24,
author={Hifza Khalid and Peter Portante and Alva Couch},
title={Linux Configuration Tuning: Is Having a Large Dataset Enough?},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2024},
pages={771-778},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012387200003654},
isbn={978-989-758-684-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Linux Configuration Tuning: Is Having a Large Dataset Enough?
SN - 978-989-758-684-2
IS - 2184-4313
AU - Khalid, H.
AU - Portante, P.
AU - Couch, A.
PY - 2024
SP - 771
EP - 778
DO - 10.5220/0012387200003654
PB - SciTePress