article

Analyzing a web-based system's performance measures at multiple time scales

Authors:

Virgilio Almeida,

Jerry RoliaAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 30, Issue 2

Pages 3 - 9

https://doi.org/10.1145/588160.588162

Published: 01 September 2002 Publication History

Abstract

Web and e-commerce workloads are known to vary significantly from hour to hour, day to day, and week to week. The causes of these fluctuations are changes in the number of users visiting a site and the mix of services they require. Since the workloads are known to vary over time, one should not simply choose an arbitrary time interval and consider it as a reference for performance evaluation. We conclude that times scales are of great importance for operational analysis, particularly for systems with bursty loads. Service level agreements must certainly take into account measurement time scales. Similarly input parameters for predictive models are sensitive to time scale. Ultimately, a time scale should be chosen for service level requirements that best expresses the needs of end-users and the price the owner of a site is willing to pay for QoS.

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Cited By

Golshani EAshtiani M(2021)Proactive auto-scaling for cloud environments using temporal convolutional neural networksJournal of Parallel and Distributed Computing10.1016/j.jpdc.2021.04.006154(119-141)Online publication date: Aug-2021
https://doi.org/10.1016/j.jpdc.2021.04.006
Ullah ALi JHussain A(2020)Design and evaluation of a biologically-inspired cloud elasticity frameworkCluster Computing10.1007/s10586-020-03073-723:4(3095-3117)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s10586-020-03073-7
Amannejad YKrishnamurthy DFar B(2019)Prospective: A Data-Driven Technique to Predict Web Service Response Time PercentilesIEEE Access10.1109/ACCESS.2019.29398057(127904-127919)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2939805
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cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 30, Issue 2

September 2002

56 pages

ISSN:0163-5999

DOI:10.1145/588160

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Copyright © 2002 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 September 2002

Published in SIGMETRICS Volume 30, Issue 2

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Cited By

Golshani EAshtiani M(2021)Proactive auto-scaling for cloud environments using temporal convolutional neural networksJournal of Parallel and Distributed Computing10.1016/j.jpdc.2021.04.006154(119-141)Online publication date: Aug-2021
https://doi.org/10.1016/j.jpdc.2021.04.006
Ullah ALi JHussain A(2020)Design and evaluation of a biologically-inspired cloud elasticity frameworkCluster Computing10.1007/s10586-020-03073-723:4(3095-3117)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s10586-020-03073-7
Amannejad YKrishnamurthy DFar B(2019)Prospective: A Data-Driven Technique to Predict Web Service Response Time PercentilesIEEE Access10.1109/ACCESS.2019.29398057(127904-127919)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2939805
Bibal Benifa JDejey D(2019)RLPASMobile Networks and Applications10.1007/s11036-018-0996-024:4(1348-1363)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s11036-018-0996-0
Jianwei Yin Xingjian Lu Xinkui Zhao Hanwei Chen Xue Liu (2015)BURSE: A Bursty and Self-Similar Workload Generator for Cloud ComputingIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2014.231520426:3(668-680)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1109/TPDS.2014.2315204
Kalyvianaki ECharalambous THand S(2014)Adaptive Resource Provisioning for Virtualized Servers Using Kalman FiltersACM Transactions on Autonomous and Adaptive Systems10.1145/26262909:2(1-35)Online publication date: 1-Jul-2014
https://dl.acm.org/doi/10.1145/2626290
Zhang-Jian DLee CHwang R(2014)An energy-saving algorithm for cloud resource management using a Kalman filterInternational Journal of Communication Systems10.1002/dac.259927:12(4078-4091)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1002/dac.2599
Krishnamurthy DRolia JXu M(2011)WAM—The Weighted Average Method for Predicting the Performance of Systems with Bursts of Customer SessionsIEEE Transactions on Software Engineering10.1109/TSE.2011.6537:5(718-735)Online publication date: 1-Sep-2011
https://dl.acm.org/doi/10.1109/TSE.2011.65
Mi NCasale GCherkasova LSmirni E(2010)Sizing multi-tier systems with temporal dependence: benchmarks and analytic modelsJournal of Internet Services and Applications10.1007/s13174-010-0012-91:2(117-134)Online publication date: 21-Sep-2010
https://doi.org/10.1007/s13174-010-0012-9
Mi NCasale GCherkasova LSmirni EDobson SStrassner JParashar MShehory O(2009)Injecting realistic burstiness to a traditional client-server benchmarkProceedings of the 6th international conference on Autonomic computing10.1145/1555228.1555267(149-158)Online publication date: 15-Jun-2009
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