Interactive learning of alert signatures in High Performance Cluster system logs

Abstract

The ability to automatically discover error conditions with little human input is a feature lacking in most modern computer systems and networks. However, with the ever increasing size and complexity of modern systems, such a feature will become a necessity in the not too distant future. Our work proposes a hybrid framework that allows High Performance Clusters (HPC) to detect error conditions in their logs. Through the use of anomaly detection, the system is able to detect portions of the log that are likely to contain errors (anomalies). Via visualization, human administrators can inspect these anomalies and assign labels to clusters that correlate with error conditions. The system can then learn a signature from the confirmed anomalies, which it uses to detect future occurrences of the error condition. Our evaluations show the system is able to generate simple and accurate signatures using very little data.