2026-09-19T09:49:40Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/465072022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Sridhar Rajagopal and Larry Rudolph.
Rogal, Adam (Adam R.)
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2009-08-26T16:38:43Z
2009-08-26T16:38:43Z
2008
2008
http://hdl.handle.net/1721.1/46507
403948369
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
Includes bibliographical references (leaves 49-50).
The research in this work addresses the need for a warning system to predict future application failures. PreCog, the predictive and regressional error correlating guide system, aims to aid administrators by providing a robust future failure warning system statistically induced from past system behavior. In this work, we show that with the use of machine learning techniques such as Adaptive Boosting and Correlation-based Feature Selection, PreCog, without any prior knowledge of its target, can be accurately and reliably trained within a virtual environment using past system metrics to predict future application in a variety of domains.
by Adam Rogal.
M.Eng.
58 leaves
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.
PreCog : a robust machine learning system to predict failure in a virtualized environment
Pre Cog : a robust machine learning system to predict failure in a virtualized environment
Robust machine learning system to predict failure in a virtualized environment
Thesis
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