| Issue |
E3S Web Conf.
Volume 419, 2023
V International Scientific Forum on Computer and Energy Sciences (WFCES 2023)
|
|
|---|---|---|
| Article Number | 01029 | |
| Number of page(s) | 13 | |
| Section | Energy Sciences, Engineering and Industry | |
| DOI | https://doi.org/10.1051/e3sconf/202341901029 | |
| Published online | 25 August 2023 | |
Methods for analyse performance in distributed systems
MIREA, Russian Technology University, 119454 Moscow, Russia
* Corresponding author: sukhoplyuev.d.i@edu.mirea.ru
The purpose of this article is to analyze various methods for measuring central tendency in statistics, including arithmetic mean, median, winsorized mean, outlier exclusion method, Hodges-Lehmann estimator, and quantile estimation and much more. The advantages and disadvantages of each of these methods are discussed, as well as their practical applications in performance analysis in distributed systems. In particular, we focus on the importance of selecting an appropriate measure of central tendency that is robust to outliers and accurately reflects the distribution of the data. We also provide examples of how these methods can be applied to real-world datasets to gain insights into the underlying patterns and trends. Overall, this article provides a comprehensive overview of the different techniques for measuring central tendency and offers practical guidance for researchers and analysts looking to make informed decisions about perfomance analysis.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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