NDLI: Issues in Bottleneck Detection in Multi-Tier Enterprise Applications

Content Provider	IEEE Xplore Digital Library
Author	Parekh, J. Jung, G. Swint, G. Pu, C. Sahai, A.
Copyright Year	2006
Description	Author affiliation: Coll. of Comput., Georgia Inst. of Technol., Atlanta, GA (Parekh, J.; Jung, G.; Swint, G.; Pu, C.)
Abstract	In this work, the performance of various machine learning classifiers with regard to bottleneck detection in enterprise, multi-tier applications governed by service level objectives is described. Specifically, in this paper, it demonstrates the effectiveness of three classifiers, a tree-augmented Naive Bayesian network, a J48 decision tree, and LogitBoost, using our bottleneck detection process, which delves into a new area of performance analysis based on the trends of metrics (first order derivative) rather than the metric value itself. Furthermore, the efficiency of each classifier by measuring the convergence speed, or the number of staging trials required in order to provide positive results is illustrated. Finally, the effectiveness of the classifiers used in the bottleneck detection process as each classifier strongly identifies the enterprise system bottleneck
Starting Page	302
Ending Page	303
File Size	196054
Page Count	2
File Format	PDF
ISBN	1424404762
ISSN	1548615X
DOI	10.1109/IWQOS.2006.250489
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning Production Automation Delay Performance analysis Large-scale systems Life testing Monitoring Yarn Automatic testing performance analysis Bottleneck detection machine learning multi-tier enterprise systems
Content Type	Text
Resource Type	Article

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