PreCog : a robust machine learning system to predict failure in a virtualized environment
Author(s)Rogal, Adam (Adam R.)
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
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Sridhar Rajagopal and Larry Rudolph.
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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.
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (leaves 49-50).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.