DC Health - Node performance data from a real datacenter of the Instituto Metropole Digital/UFRN

This work aims to identify anomalous patterns, that could be associated with and performance degradation and failures, in datacenter nodes such as Virtual Machines or Virtual Machines clusters. The early detection of anomalies can enable remediation measures, such as Virtual Machines migration and resource reallocation, before losses occur. One way to detect anomalous patterns in datacenter nodes is using monitoring data from the nodes, such as CPU and memory utilization. To assist the common challenge in the field of anomaly detection and online anomaly detection, that is the unavailability of labeled data, this dataset contains unlabeled real node performance data. The files were composed of a dataset group for each compute cluster (clusters 1 to 6), a dataset for a controller cluster and one for the storage node. The files were collated from rosts over a year (2021 to 2022).