Spatial-Temporal Learning-Based Artificial Intelligence for IT Operations in the Edge Network

Abstract

With the rapid increase of edge network scale and the complexity of service interaction, it takes more time for operation staff to analyze anomalies from complex scenarios. To maintain the normal network operation, various key performance indicators, such as link delay, throughput, and memory usage, are monitored for timely anomaly detection and troubleshooting. We introduce artificial intelligence for IT operations to assist operators in performing anomaly detection, anomaly localization, and root cause analysis, and building an intelligent operation and maintenance platform over the software-defined networking (SON)-based edge network. In this article, the graph-based gated convolutional network for anomaly detection (GAD) is first proposed to solve the anomaly detection problem of time series data with topology information. Specifically, GAD uses a gated convolutional encoder to encode spatial-temporal time series, and a graph convolutional network is developed to capture the spatial dependence. Then, based on the features, a convolutional layer is used to decode features and reconstruct input sequence. Finally, the residual between input and reconstructed sequences is further utilized to detect anomalies. Our experimental results demonstrate that GAD outperforms the state-of-the-art anomaly detection baselines in terms of F-scores on the datasets collected by an SDN simulation platform.