Methods of Descriptive Statistics in Telemetry Tasks

This discourse investigates methodologies for the computation of quantile estimates and descriptive statistics within the framework of telemetry challenges. This undertaking gains significance in the planning and execution of surveillance systems, particularly in scenarios involving heavily burdened distributed systems or the deployment of load balancers. This pertinence arises due to the pivotal role played by limited computational resources in such systems. Concretely, this article delves into approaches encompassing moving quantile estimates, the square P quantile estimator, the data structure of partitioning heaps, and methods such as the Median Absolute Deviation (MAD) and Shamos estimates to address variations. The elucidation herein is intended to contribute to the advancement of precision and efficiency in the realm of telemetry problem-solving.