Intelligent network operation platform for network fault mitigation
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.
1. A method for diagnosing and resolving network incidents, the method comprising:
receiving, by one or more processors, an alarm message comprising information indicative of a network incident that occurred in a network;
executing, by the one or more processors, machine learning logic against the information indicative of the network incident and a network incident cause model to determine one or more candidate actions,
the one or more candidate actions determined to resolve a cause of the network incident, and
the network incident cause model derived from a plurality of historic network data clusters that are each associated with a corresponding network incident cause; and
executing, by the one or more processors, at least one candidate action of the one or more candidate actions.
2. The method of claim 1 , wherein each historic network data cluster of the plurality of historic network data clusters corresponds to a set of historic network incidents associated with a same network incident cause.
3. The method of claim 2 , further comprising identifying a historic network data cluster corresponding to the network incident based on similarities between the network incident and the set of historic network incidents corresponding to the historic network data cluster.
4. The method of claim 3 , wherein the information indicative of the network incident comprises a plurality of parameters and the similarities are based on the plurality of parameters.
5. The method of claim 1 , wherein the at least one candidate action is executed automatically.
6. The method of claim 1 , further comprising transmitting a notification to a user device that includes information that identifies the one or more candidate actions,
wherein the at least one candidate action is executed in response to an input received from the user device.
7. The method of claim 6 , wherein the input corresponds to activation of an interactive element presented within a graphical user interface of the user device.
8. The method of claim 1 , further comprising:
monitoring the network associated with the network incident to determine whether the execution of the at least one candidate action resolved a cause of the network incident; and
executing another action of the one or more candidate actions upon a determination, based on the monitoring, that the execution of the at least one candidate action did not resolve the cause of the network incident.
9. The method of claim 1 , further comprising:
generating feedback data based on the execution of the at least one candidate action; and
updating the plurality of historic network data clusters based on the feedback data.
10. The method of claim 9 , further comprising further training the network incident cause model based on the feedback data.
11. The method of claim 1 , further comprising assigning a score for each candidate action of the one or more candidate actions, wherein the at least one candidate action is selected for the execution based at least in part on a score assigned to the at least one candidate action.
12. The method of claim 1 , further comprising determining a classification for the network incident,
wherein the classification is selected from a plurality of classifications, and
wherein executing the at least one candidate action is based at least in part on the classification of the at least one candidate action.
13. A system comprising:
a memory; and
one or more processors communicatively coupled to the memory and configured to:
receive an alarm message comprising information indicative of a network incident that occurred in a network;
execute machine learning logic against the information indicative of the network incident and a network incident cause model to determine one or more candidate actions,
the one or more candidate actions determined to resolve a cause of the network incident, and
the network incident cause model derived from a plurality of historic network data clusters that are each associated with a corresponding network incident cause; and
execute at least one candidate action of the one or more candidate actions.
14. The system of claim 13 , wherein:
each historic network data cluster of the plurality of historic network data clusters corresponds to a set of historic network incidents associated with a same network incident cause; and
the one or more processors are configured to identify a historic network data cluster corresponding to the network incident based on similarities between the network incident and the set of historic network incidents corresponding to the historic network data cluster.
15. The system of claim 14 , wherein the one or more processors are further configured to:
determine one or more actions executed to resolve a cause of the set of historic network incidents, wherein the one or more candidate actions correspond to the one or more actions executed to resolve the cause of the set of historic network incidents.
16. The system of claim 13 , wherein the one or more processors are configured to:
monitor the network associated with the network incident to determine whether the execution of the at least one candidate action resolved a cause of the network incident; and
execute another action of the one or more candidate actions upon a determination, based on the monitoring, that the execution of the at least one candidate action did not resolve the cause of the network incident.
17. The system of claim 13 , wherein the one or more processors are configured to:
generate feedback data based on the execution of the at least one candidate action;
update the plurality of historic network data clusters based on the feedback data; and
update the network incident cause model based on the updated plurality of historic network data clusters.
18. A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
receiving an alarm message comprising information indicative of a network incident that occurred in a network;
executing machine learning logic against the information indicative of the network incident and a network incident cause model to determine one or more candidate actions,
the one or more candidate actions determined to resolve a cause of the network incident, and
the network incident cause model derived from a plurality of historic network data clusters that are each associated with a corresponding network incident cause; and
executing at least one candidate action of the one or more candidate actions.
19. The non-transitory computer-readable storage medium of claim 18 , wherein the executing of the at least one candidate action resolves the cause of the network incident.
20. The non-transitory computer-readable storage medium of claim 18 , the operations further comprising:
generating feedback data based on the execution of the at least one candidate action;
updating the plurality of historic network data clusters based on the feedback data; and
training the network incident cause model based on the updated plurality of historic network data clusters.