Linux Configuration Tuning: Is Having a Large Dataset Enough?

Hifza Khalid, Peter Portante, Alva Couch

2024

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.

Paper Citation

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Linux Configuration Tuning: Is Having a Large Dataset Enough?
SN - 978-989-758-684-2
AU - Khalid H.
AU - Portante P.
AU - Couch A.
PY - 2024
SP - 771
EP - 778
DO - 10.5220/0012387200003654
PB - SciTePress

in Harvard Style

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 - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 771-778. DOI: 10.5220/0012387200003654

in Bibtex Style

@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 - Volume 1: ICPRAM},
year={2024},
pages={771-778},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012387200003654},
isbn={978-989-758-684-2},
}