System failure prediction using long short-term memory neural networks
First Claim
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1. A method for system failure prediction, comprising:
- clustering a plurality of log files according to structural log patterns to form log clusters;
determining feature representations of the log files based on the log clusters;
training a long short-term memory recurrent neural network from training data and user labels on failure time periods;
determining a likelihood of a system failure based on the feature representations using a neural network by processing the feature representations using the long short-term memory recurrent neural network; and
performing an automatic system control action if the likelihood of the system failure exceeds a threshold.
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Abstract
Methods for system failure prediction include clustering log files according to structural log patterns. Feature representations of the log files are determined based on the log clusters. A likelihood of a system failure is determined based on the feature representations using a neural network. An automatic system control action is performed if the likelihood of system failure exceeds a threshold.
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Citations
15 Claims
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1. A method for system failure prediction, comprising:
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clustering a plurality of log files according to structural log patterns to form log clusters; determining feature representations of the log files based on the log clusters; training a long short-term memory recurrent neural network from training data and user labels on failure time periods; determining a likelihood of a system failure based on the feature representations using a neural network by processing the feature representations using the long short-term memory recurrent neural network; and performing an automatic system control action if the likelihood of the system failure exceeds a threshold. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method for system failure prediction, comprising:
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clustering a plurality of log files having heterogeneous log formats according to structural log patterns using unsupervised, hierarchical clustering to form log clusters; determining feature representations of the log files based on the log clusters, wherein the feature representations consist of pattern distribution among clustered logs and term frequency-inverse document frequency; training a long short-term memory recurrent neural network from training data and user labels on failure time periods; determining a likelihood of a system failure based on the feature representations using the long short-term memory neural network, a binomial distribution based on an output of the long short-term memory neural network, and a binary target vector with two complementary classes by processing the feature representations using the long short-term memory recurrent neural network; and performing an automatic system control action if the likelihood of the system failure exceeds a threshold.
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9. A system for system failure prediction, comprising:
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a long-short term memory recurrent neural network that is trained from training data and user labels on failure time periods; a pattern learning module comprising a processor configured to cluster a plurality of log files according to structural log patterns to form log clusters; a feature extraction module configured to determine feature representations of the log files based on the log clusters; a failure prediction module configured to determine a likelihood of a system failure based on the feature representations by processing the feature representations using the long short-term memory recurrent neural network; and a system control module configured to perform an automatic system control action if the likelihood of the system failure exceeds a threshold. - View Dependent Claims (10, 11, 12, 13, 14, 15)
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Specification