Application of Support Vector Machine approach to Chinook failure prediction
EMSL Project ID
33195
Abstract
Mitigating the impact of computer failure is possible if accurate failure predictions are provided. This research will provide a proof of concept implementation of a new spectrum-kernel Support Vector Machine (SVM) approach to predict failure events based on system log files and other data. Experimental results using actual system log files from the MPP2 Linux-based compute cluster indicate the proposed SVM approach can predict hard disk failure with an accuracy of 76% as much as 48 hours in advance. We propose to apply similar analysis to Chinook, developing new techniques and studying the effects of a larger-scale system. The payoff for this research will be increased reliability of HPC systems and less expense from time wasted due to component failures.Project Details
Start Date
2009-01-26
End Date
2012-01-29
Status
Closed
Released Data Link
Team
Principal Investigator
Related Publications
Fulp EW, GA Fink, and JN Haack. 2008. "Predicting Computer System Failures Using Support Vector Machines." In First USENIX Workshop on the Analysis of System Logs (WASL '08). USENIX, Berkeley, CA.