Ranking Anomalous High Performance Computing Sensor Data Using Unsupervised Clustering

Environmental sensors monitor supercomputing facility health, generating massive data in the largest facilities. Current state-of-the-art is for human operators to evaluate environmental data by hand. This approach will not be viable on Exascale machines, nor is it ideal on current systems. We evaluate effectiveness of the DBSCAN algorithm for identifying anomalies in supercomputing sensor data. We filter large portions of data showing normal behavior from anomalies, and then rank anomalous points by distance to the nearest normal cluster. We compare DBSCAN to k-means and Gaussian kernel density estimation, finding that DBSCAN effectively clusters sensor data from a Cray supercomputing facility. DBSCAN also successfully clusters synthetic injected data, avoiding the false positives generated by k-means and Gaussian kernel density estimation.