article

Web server performance analysis using histogram workload models

Authors:

Enrique Hernández-Orallo,

Joan Vila-CarbóAuthors Info & Claims

Computer Networks: The International Journal of Computer and Telecommunications Networking, Volume 53, Issue 15

Pages 2727 - 2739

https://doi.org/10.1016/j.comnet.2009.06.005

Published: 01 October 2009 Publication History

Abstract

Web servers are required to perform millions of transaction requests per day at an acceptable Quality of Service (QoS) level in terms of client response time and server throughput. Consequently, a thorough understanding of the performance capabilities and limitations of web servers is critical. Finding a simple web traffic model described by a reasonable number of parameters that enables powerful analysis methods and provides accurate results has been a challenging problem during the last few decades. This paper proposes a discrete statistical description of web traffic that is based on histograms. In order to reflect the second-order statistics (long-range dependence and self-similarity) of the workload, this basic model has been extended using the Hurst parameter. Then, a system performance model-based on histogram operators (histogram calculus) is introduced. The proposed model has been evaluated using real workload traces using a single-site server model. These evaluations show that the model is accurate and improves the results of classic queueing models. The model provides an excellent basis for a decision support tool to predict the behavior of web servers.

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Digital Library

Cited By

You GWang X(2020)A server-side accelerator framework for multi-core CPUs and Intel Xeon Phi co-processor systemsCluster Computing10.1007/s10586-019-03030-z23:4(2591-2608)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s10586-019-03030-z
Ahmad TTruscan DPorres I(2018)Identifying worst-case user scenarios for performance testing of web applications using Markov-chain workload modelsFuture Generation Computer Systems10.1016/j.future.2018.01.04287:C(910-920)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1016/j.future.2018.01.042
Aït-Salaht FCastel-Taleb HFourneau JPekergin N(2017)Stochastic Bounds for Switched Bernoulli Batch Arrivals Observed Through MeasurementsAnalytical and Stochastic Modelling Techniques and Applications10.1007/978-3-319-61428-1_1(1-15)Online publication date: 10-Jul-2017
https://dl.acm.org/doi/10.1007/978-3-319-61428-1_1
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Web server performance analysis using histogram workload models
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Published In

cover image Computer Networks: The International Journal of Computer and Telecommunications Networking

Computer Networks: The International Journal of Computer and Telecommunications Networking Volume 53, Issue 15

October, 2009

152 pages

ISSN:1389-1286

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Copyright © Elsevier B.V. © 2009.

Publisher

Elsevier North-Holland, Inc.

United States

Publication History

Published: 01 October 2009

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Cited By

You GWang X(2020)A server-side accelerator framework for multi-core CPUs and Intel Xeon Phi co-processor systemsCluster Computing10.1007/s10586-019-03030-z23:4(2591-2608)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s10586-019-03030-z
Ahmad TTruscan DPorres I(2018)Identifying worst-case user scenarios for performance testing of web applications using Markov-chain workload modelsFuture Generation Computer Systems10.1016/j.future.2018.01.04287:C(910-920)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1016/j.future.2018.01.042
Aït-Salaht FCastel-Taleb HFourneau JPekergin N(2017)Stochastic Bounds for Switched Bernoulli Batch Arrivals Observed Through MeasurementsAnalytical and Stochastic Modelling Techniques and Applications10.1007/978-3-319-61428-1_1(1-15)Online publication date: 10-Jul-2017
https://dl.acm.org/doi/10.1007/978-3-319-61428-1_1
Mishra AHellerstein JCirne WDas C(2010)Towards characterizing cloud backend workloadsACM SIGMETRICS Performance Evaluation Review10.1145/1773394.177340037:4(34-41)Online publication date: 27-Mar-2010
https://dl.acm.org/doi/10.1145/1773394.1773400
Palshikar GAhuja AVin H(2010)Utilization analysis of servers in a data centreProceedings of the Second international conference on Data Engineering and Management10.1007/978-3-642-27872-3_26(173-180)Online publication date: 29-Jul-2010
https://dl.acm.org/doi/10.1007/978-3-642-27872-3_26

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