Abstract: Systems and methods include receiving alarms from a network; utilizing a machine learning model to classify the alarms as one of important and non-important; and displaying the important alarms and suppressing display of the non-important alarms. The systems and methods can further include training the machine learning model with historical alarm data that includes features related to an associated device and comments related to how a Network Operations Center (NOC) handles an associated alarm or group of alarms. The training can be via supervised machine learning with the features used as labels or via reinforcement learning with the features used as a reward.

Abstract: Systems and methods for discovering the connectivity topology of cross-layer links in a multi-layer network are provided. In one implementation, a method includes a step of obtaining input data related to a plurality of Network Elements (NEs) operating in a plurality of layers within a multi-layer network having one or more cross-layer port-to-port connectivity links therebetween. The method also includes the step of utilizing Machine Learning (ML) processes and the input data to discover the one or more cross-layer port-to-port connectivity links between pairs of NEs operating in different layers of the plurality of layers within the network.