Trade-Offs in Implementing Unsupervised Anomaly Detection with TAPI-Based Streaming Telemetry

It is essential to be able to identify hidden anomalies in order to fully automate optical networks. This requires specific features from the application programming interfaces (APIs) used by the control plane and network monitoring solution. One of the solutions, Transport API (TAPI), utilizes advanced techniques in telemetry streaming. The update policy in TAPI enables key performance indicators (KPIs) to be transmitted only when changes are detected. In this paper, we explore how the update policy configuration of TAPI and the use of unsupervised learning (UL) interact in detecting previously unseen anomalies. Results reveal various trade-offs that network operators need to consider, including compute and time overhead, as well as the overall accuracy of UL.