Efficient anomaly workflow detection for end-to-end provisioning systems

A method implemented in a network system to provide efficient anomaly detection simultaneously across elements along end-to-end provisioning flows in the network system, the method comprising:

• provisioning, by a network provisioning application implemented in a first computer system, a plurality of provisioning flows, each based on a respective one of a plurality of transactions associated with a respective one of a plurality of transaction types and a respective one of a plurality of subscriber segments, wherein the provisioning comprises:
• transmitting, by the network provisioning application, to a plurality of network elements (NEs), provisioning instructions based on a translation of the plurality of transactions, wherein the translation is based on a plurality of network provisioning catalogs (NPCs);
• receiving, by an anomaly detection application implemented in a second computer system, a provisioning log captured from provisioning respective ones of the provisioning flows over a predefined time period, wherein the provisioning log comprises operations and response codes associated with provisioning flow elements along the plurality of provisioning flows, wherein the provisioning flow elements comprise the plurality of transaction types, the plurality of subscriber segments, the plurality of NPCs, and the plurality of NEs;
• determining, by the anomaly detection application, based on the provisioning log, a plurality of metrics, each indicative of a success rate for a respective one of the provisioning flow elements in association with provisioning a respective one of the plurality of provisioning flows;
• determining, by the anomaly detection application, a machine learning (ML) model to approximate a probability density function of the plurality of metrics;
• determining, by the anomaly detection application, an anomaly at one or more of the provisioning flow elements based on the ML model;
• and
• triggering, by the anomaly detection application, an action to address the detected anomaly.