{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/46507"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/46507","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"PreCog : a robust machine learning system to predict failure in a virtualized environment","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Rogal, Adam (Adam R.)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Sridhar Rajagopal and Larry Rudolph."],"committee_chairs":[],"committee_members":[],"year":2008,"date_issued":"2008","date_published":"2008","updated_at":"2026-07-22T22:21:52Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/46507","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Sridhar Rajagopal and Larry Rudolph."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Rogal, Adam (Adam R.)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2009-08-26T16:38:43Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2009-08-26T16:38:43Z"]},{"key":"dc:date.issued","label":"Date","values":["2008"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering and Computer Science."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["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."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/46507"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.","Includes bibliographical references (leaves 49-50)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["PreCog : a robust machine learning system to predict failure in a virtualized environment"]}]}],"canonical_facts":{"dc:contributor.advisor":["Sridhar Rajagopal and Larry Rudolph."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:creator":["Rogal, Adam (Adam R.)"],"dc:date.accessioned":["2009-08-26T16:38:43Z"],"dc:date.available":["2009-08-26T16:38:43Z"],"dc:date.issued":["2008"],"dc:description":["Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.","Includes bibliographical references (leaves 49-50)."],"dc:description.abstract":["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."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/46507"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["PreCog : a robust machine learning system to predict failure in a virtualized environment"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:52Z"}