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Predicting intermittent network device failures based on network metrics from multiple datasources

Predicting failures in networking has been a subject for a long time. Time series data and syslog are now often used to monitor the state of the network. SURFnet has been collecting this data in searchable database clusters for over two years. We set out to determine to what extent these two datasets can be used to predict network device failures. This enables network operators to take pre-emptive measures to prevent an imminent failure. Using experiments, we proved a relation between the dataset containing the syslog device events and the dataset containing the time-series network metrics.