Data-Driven Construction of Data Center Graph of Things for Anomaly Detection
This work addresses anomaly detection in data centers, which is critical for operational reliability, but it is incremental as it applies existing graph neural network techniques to a new domain-specific sensor graph.
The paper tackles the problem of monitoring data centers by constructing a sensor graph from time series data to capture relationships between sensors, and demonstrates that using a graph neural network for anomaly detection outperforms existing methods by a factor of 2 to 3 in precision and F1 score.
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.