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Zhelong Pan and Robert T. Morris. Stunes, Michael R Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2013-02-14T15:38:44Z 2013-02-14T15:38:44Z 2012 2012 http://hdl.handle.net/1721.1/77017 825769580 Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012. Cataloged from PDF version of thesis. Includes bibliographical references (p. 59-60). This thesis describes an algorithm developed to aid in solving the problem of performance diagnosis, by automatically identifying the specific component in a multicomponent application system responsible for a performance problem. The algorithm monitors the system, collecting load and latency information from each component, searches the data for patterns indicative of performance saturation using statistical methods, and uses a machine learning classifier to interpret those results. The algorithm was tested with two test applications in several configurations, with different performance problems synthetically introduced. The algorithm correctly located these problems as much as 90% of the time, indicating that this is a good approach to the problem of automatic performance problem location. Also, the experimentation demonstrated that the algorithm can locate performance problems in environments different from those for which it was designed and from that on which it was trained. by Michael R. Stunes. M.Eng. 60 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Statistical methods for locating performance problems in multi-tier applications Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="804af1d5-6be4-4d6c-8704-2aabb137173d"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Statistical methods for locating performance problems in multi-tier applications</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2012</PublicationDate> <Authors> <Author> <DisplayName>Stunes, Michael R</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>This thesis describes an algorithm developed to aid in solving the problem of performance diagnosis, by automatically identifying the specific component in a multicomponent application system responsible for a performance problem. The algorithm monitors the system, collecting load and latency information from each component, searches the data for patterns indicative of performance saturation using statistical methods, and uses a machine learning classifier to interpret those results. The algorithm was tested with two test applications in several configurations, with different performance problems synthetically introduced. The algorithm correctly located these problems as much as 90% of the time, indicating that this is a good approach to the problem of automatic performance problem location. Also, the experimentation demonstrated that the algorithm can locate performance problems in environments different from those for which it was designed and from that on which it was trained.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>