Locating Datacenter Link Faults with a Directed Graph Convolutional Neural Network

Datacenters alongside many domains are well represented by directed graphs, and there are many datacenter problems where deeply learned graph models may prove advantageous. Yet few applications of graph-based convolutional neural networks (GCNNs) to datacenters exist. Few of the GCNNs in the literature are explicitly designed for directed graphs, partly owed to the relative dearth of GCNNs designed specifically for directed graphs. We present therefore a convolutional operation for directed graphs, which we apply to learning to locate the faulty links in datacenters. Moreover, since the detection problem would be phrased as link-wise classification, we propose constructing a directed linegraph, where the problem is instead phrased as a vertex-wise classification. We find that our model detects more link faults than the comparison models, as measured by McNemar's test, and outperforms the comparison models in respect of the F-1-score, precision and recall.