Causality detection for outlier events in telemetry metric data

Abstract

Identifying causal relationships between outlier telemetry events in telemetry metric data using machine learning ensembles of an autoencoder and an attention mechanism provides an automated framework for root cause analysis. Outlier telemetry events are detected across a cloud of telemetry events using unsupervised learning models. To establish a causal relationship between outlier telemetry events, autoencoder/attention mechanism ensembles are trained for pairs of telemetry metrics. When inputs of sequences of telemetry events of a first telemetry metric and a second telemetry metric to the ensemble have sufficiently high loss value, a causal relationship is inferred. Internal node values of the attention mechanism from the input identify specific time stamps for the first telemetry metric that have a causal relationship with the outlier telemetry event.