Abstract: An exemplary method, for preventing an outage in a network, includes collecting network metrics and producing statistical features of the network during a predetermined time window based on statistical analysis of the collected metrics. The method further includes selecting, from the produced statistical features, a first data set of relevant statistical features. Further, the method includes collecting information about a past event, extracting event data from the past event, and vectorizing the extracted event data to form a second data set. The method further includes concatenating the first data set and the second data set to form a third data set and classifying the third data set relative to a model of historical network performance to determine a probability of the outage. Further, the method includes modifying, in response to the probability being above a threshold, the network/activity within the network to avoid onset of the outage.

Abstract: An exemplary method, for solving a problem with a network element within a network, includes first selecting a first set of pre-existing bots and first executing at least some of the selected first set of bots to change the network element and/or the network to solve the problem. The method further includes conducting a root cause analysis to generate recommendation(s) to correct the problem and in response to the first executing failing to solve the problem: second selecting, based on the recommendation(s), a second set of pre-existing bots to change the network element and/or the network to solve the problem, and second executing at least some of the selected second set of bots to solve the problem. Subsequently, the method includes generating, in response to the second executing failing to solve the problem, instructions to manually change the network element and/or the network to solve the problem.

Abstract: A method is provided in an example embodiment and may include receiving information identifying an anomaly or a predicted outage of a component of a system and requesting a data service for buffered data generated by the component within a timeframe of receiving the information. The method further may include normalizing the buffered data into discrete time units into corresponding distinct units of time and analyzing the buffered data using a machine learning (ML) model to identify a root cause of the anomaly or the predicted outage. The method further may include identifying at least one solution to the identified root cause based on characteristics associated with the root cause of the anomaly or the predicted outage and providing the at least one solution to an automation component to avoid onset of an event that triggers outage of the component or the system.

Abstract: An exemplary method, for preventing an outage in a network, includes collecting network metrics and producing statistical features of the network during a predetermined time window based on statistical analysis of the collected metrics. The method further includes selecting, from the produced statistical features, a first data set of relevant statistical features. Further, the method includes collecting information about a past event, extracting event data from the past event, and vectorizing the extracted event data to form a second data set. The method further includes concatenating the first data set and the second data set to form a third data set and classifying the third data set relative to a model of historical network performance to determine a probability of the outage. Further, the method includes modifying, in response to the probability being above a threshold, the network/activity within the network to avoid onset of the outage.