Open Access
November 2006 Monitoring Networked Applications With Incremental Quantile Estimation
John M. Chambers, David A. James, Diane Lambert, Scott Vander Wiel
Statist. Sci. 21(4): 463-475 (November 2006). DOI: 10.1214/088342306000000583


Networked applications have software components that reside on different computers. Email, for example, has database, processing, and user interface components that can be distributed across a network and shared by users in different locations or work groups. End-to-end performance and reliability metrics describe the software quality experienced by these groups of users, taking into account all the software components in the pipeline. Each user produces only some of the data needed to understand the quality of the application for the group, so group performance metrics are obtained by combining summary statistics that each end computer periodically (and automatically) sends to a central server. The group quality metrics usually focus on medians and tail quantiles rather than on averages. Distributed quantile estimation is challenging, though, especially when passing large amounts of data around the network solely to compute quality metrics is undesirable. This paper describes an Incremental Quantile (IQ) estimation method that is designed for performance monitoring at arbitrary levels of network aggregation and time resolution when only a limited amount of data can be transferred. Applications to both real and simulated data are provided.


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John M. Chambers. David A. James. Diane Lambert. Scott Vander Wiel. "Monitoring Networked Applications With Incremental Quantile Estimation." Statist. Sci. 21 (4) 463 - 475, November 2006.


Published: November 2006
First available in Project Euclid: 23 April 2007

zbMATH: 05191835
MathSciNet: MR2380709
Digital Object Identifier: 10.1214/088342306000000583

Keywords: Aggregated data , data stream , Performance monitoring , reliability

Rights: Copyright © 2006 Institute of Mathematical Statistics

Vol.21 • No. 4 • November 2006
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