Inter-and-Intra Domain Attention Relational Inference for Rack Temperature Prediction in Data Center

In a data center, predicting the rack temperature then generating alarms when an exception is detected can prevent server failure caused by high rack temperature. Each measuring point records the temperature of the rack over time, and each pair of measuring points may be associated with services or locations. Therefore, the rack temperature prediction problem can be modeled as a graph-based prediction problem. In this case, the prediction of the rack temperature depends not only on its own historical temperature but also on the temperature of racks having the same service or located near each other. Furthermore, the temperature of the rack is actually determined by various factors such as IT workloads and cold aisle temperature. Existing graph-based prediction methods do not consider the influence of these domains during the prediction, but only consider the temperature domain itself. To overcome this challenge, we propose an Inter-and-Intra domain Attention Relational Inference (I2A-RI) model: an unsupervised model that learns the relations between time series variables from different domains and utilizes the inferred interaction structure to achieve accurate dynamical predictions. Two attention modules, the guidance domain attention (GDA) module and the intra-domain attention (IDA) module, are proposed in I2A-RI, which encodes the inter-and-intra domain information to guide the learning procedure. Experiments on the real-world rack temperature dataset show that I2A-RI outperforms other state-of-the-art models since it takes the advantage of the ability to infer the potential interactions across domains. The benefits of the two proposed attention modules are also verified in the experiments.