Methods for analyse performance in distributed systems

Abstract

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.