论文标题
数据驱动的数据中心图的构造用于异常检测
Data-Driven Construction of Data Center Graph of Things for Anomaly Detection
论文作者
论文摘要
数据中心（DC）既包含IT设备和设施设备，并且DC的操作都需要高质量的监视（异常检测）系统。 DC监视系统中的计算机室中有很多传感器，并且固有地相关。这项工作提出了一个数据驱动的管道（TS2Graph），以从传感器的时间序列测量值中构建事物的直流图（传感器图）。传感器图是一个无方向的加权属性图，其中传感器是节点，传感器特征是节点属性，而传感器连接是边缘。传感器节点属性由表征传感器事件（行为）而不是原始时间序列的特征定义。传感器连接（边缘重量）由两个传感器之间并发事件的概率定义。事物原型的图是由真实数据中心的传感器时间序列构建的，它成功揭示了传感器之间有意义的关系。为了证明使用直流传感器图用于异常检测，我们比较了图神经网络（GNN）的性能和合成异常数据的现有标准方法。 GNN的表现优于现有算法2至3（就精度和F1分数而言），因为它考虑了DC传感器之间的拓扑关系。我们希望DC传感器图可以用作直流监测系统的基础架构，因为它代表传感器关系。
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.
