  Data center (DC) contains both IT devices and facility equipment, and the
operation of a DC requires a high-quality monitoring (anomaly detection)
system. There are lots of sensors in computer rooms for the DC monitoring
system, and they are inherently related. This work proposes a data-driven
pipeline (ts2graph) to build a DC graph of things (sensor graph) from the time
series measurements of sensors. The sensor graph is an undirected weighted
property graph, where sensors are the nodes, sensor features are the node
properties, and sensor connections are the edges. The sensor node property is
defined by features that characterize the sensor events (behaviors), instead of
the original time series. The sensor connection (edge weight) is defined by the
probability of concurrent events between two sensors. A graph of things
prototype is constructed from the sensor time series of a real data center, and
it successfully reveals meaningful relationships between the sensors. To
demonstrate the use of the DC sensor graph for anomaly detection, we compare
the performance of graph neural network (GNN) and existing standard methods on
synthetic anomaly data. GNN outperforms existing algorithms by a factor of 2 to
3 (in terms of precision and F1 score), because it takes into account the
topology relationship between DC sensors. We expect that the DC sensor graph
can serve as the infrastructure for the DC monitoring system since it
represents the sensor relationships.
