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Failure analysis, modeling, and prediction for BlueGene/L
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Failure analysis, modeling, and prediction for BlueGene/L
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Liang
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Yinglung
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Yinglung Liang
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Zhang
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Yanyong
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Advisory Committee
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Yanyong Zhang
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Wade
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internal member
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Manish
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Advisory Committee
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Manish Parashar
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Xiong
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Hui
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Advisory Committee
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Hui Xiong
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Rutgers University
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Graduate School - New Brunswick
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theses
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2007
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2007
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English
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electronic
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xii, 129 pages
Abstract
The growing computational and storage needs of scientific applications mandate the deployment of extreme-scale parallel machines, such as IBM's BlueGene/L, a 64K dual-core processor system. One of the challenges of designing and deploying such systems in a production setting is the need to take failure occurrences into account. Once the large scale system equipped with a failure predictability, the fault tolerance and resource management strategies of the system can be improved significantly, and its performance can be highly increased.
This dissertation is based on the Reliability, Availability and Serviceabilit (RAS) events generated by IBM BlueGene/L over a period of 142 days. Using these logs, we performed failure analysis, modeling, and prediction. Filters are created to reveal the system failure behaviors, three preliminary models are identified for the failures, and finally, three failure predictors are established for the system. We heavily use data mining and time series analysis techniques for this dissertation. Our comprehensive evaluation demonstrates that our Bi-Modal Nearest Neighbor predictor greatly outperforms the other two (RIPPER and LIBSVM based), leading
to an F-measure of 70% and 50% for a 12-hour and 6-hour prediction window size.
This dissertation is based on the Reliability, Availability and Serviceabilit (RAS) events generated by IBM BlueGene/L over a period of 142 days. Using these logs, we performed failure analysis, modeling, and prediction. Filters are created to reveal the system failure behaviors, three preliminary models are identified for the failures, and finally, three failure predictors are established for the system. We heavily use data mining and time series analysis techniques for this dissertation. Our comprehensive evaluation demonstrates that our Bi-Modal Nearest Neighbor predictor greatly outperforms the other two (RIPPER and LIBSVM based), leading
to an F-measure of 70% and 50% for a 12-hour and 6-hour prediction window size.
Note
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Ph.D.
Note
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Includes bibliographical references (p. 123-127).
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Electrical and Computer Engineering
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Supercomputers
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Computers--Reliability
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.16726
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doi:10.7282/T32J6C8N
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ETD doctoral
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Open
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Yinglung Liang
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Rutgers University. Graduate School - New Brunswick
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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