Abstract: Provided are systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

Abstract: Embodiments of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

Abstract: Embodiments of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

Abstract: Embodiments of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

Abstract: Embodiments of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

Abstract: Embodiments of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

Abstract: A device receives first network information associated with a first portion of a network, and second network information associated with a second portion of the network, and determines, based on the first network information and the second network information, an alarm condition for the network. The device determines, based on correlation rules, whether the alarm condition relates to a currently handled alarm condition or a previously handled alarm condition. The device correlates the alarm condition with network inventory information, network topology information, and network service information, when the alarm condition does not relate to the currently handled alarm condition or the previously handled alarm condition, to generate a correlated alarm condition. The device automatically generates a resolution for the correlated alarm condition based on the correlated alarm condition, and automatically performs one or more actions based on the resolution for the correlated alarm condition.

Abstract: A device receives first network information associated with a first portion of a network, and second network information associated with a second portion of the network, and determines, based on the first network information and the second network information, an alarm condition for the network. The device determines, based on correlation rules, whether the alarm condition relates to a currently handled alarm condition or a previously handled alarm condition. The device correlates the alarm condition with network inventory information, network topology information, and network service information, when the alarm condition does not relate to the currently handled alarm condition or the previously handled alarm condition, to generate a correlated alarm condition. The device automatically generates a resolution for the correlated alarm condition based on the correlated alarm condition, and automatically performs one or more actions based on the resolution for the correlated alarm condition.