Machine Learning
Bayesian approaches to failure prediction for disk drives
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Monitoring hard disks with smart
Linux Journal
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Failure trends in a large disk drive population
FAST '07 Proceedings of the 5th USENIX conference on File and Storage Technologies
Failure Prediction in IBM BlueGene/L Event Logs
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
In-the-dark network traffic classification using support vector machines
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
Predicting computer system failures using support vector machines
WASL'08 Proceedings of the First USENIX conference on Analysis of system logs
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Mitigating the impact of computer failure is possible if accurate failure predictions are provided. Resources, and services can be scheduled around predicted failure and limit the impact. Such strategies are especially important for multi-computer systems, such as compute clusters, that experience a higher rate of failure due to the large number of components. However providing accurate predictions with sufficient lead time remains a challenging problem. This research uses a new spectrum-kernel Support Vector Machine (SVM) approach to predict failure events based on system log files. These files contain messages that represent a change of system state. While a single message in the file may not be sufficient for predicting failure, a sequence or pattern of messages may be. This approach uses a sliding window (sub-sequence) of messages to predict the likelihood of failure. Then, a frequency representation of the message sub-sequences observed are used as input to the SVM. The SVM associates the messages to a class of failed or non-failed system. Experimental results using actual system log files from a Linux-based compute cluster indicate the proposed spectrum-kernel SVM approach can predict hard disk failure with an accuracy of 80% about one day in advance.