Intelligent network operation platform for network fault mitigation

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.

Description

CROSS-REFERENCE TO RELATED APPLICATION[0001]This application claims the benefit of Indian Provisional Application No. 202041026007 filed Jun. 19, 2020, and entitled “INTELLIGENT NETWORK OPERATION PLATFORM FOR 5G NETWORKS,” the content of which is incorporated herein by reference in its entirety.TECHNICAL FIELD[0002]The present invention relates generally to network fault resolution techniques and more specifically to systems and methods that leverage machine learning and artificial intelligence to rapidly identify, diagnose, and mitigate causes of network faults.BACKGROUND OF THE INVENTION[0003]Network technology advancements have resulted in rapid deployment and adoption of network services and functionality. For example, the services and functionality provided by voice communication networks (e.g., 4th Generation (4G) and 5G communication networks), content distribution networks, enterprise and cloud-services networks, and the like have become widespread and play a vital role in t...

AI Technical Summary

Benefits of technology

[0008]As actions are executed, the system may monitor the network to ensure that the action(s) resolved the network incident. If an action did not resolve the incident, additional candidate actions may be executed until the network incident is resolved. Information associated with network incidents resolved using the techniques disclosed herein may be provided as feedback that may be incorporated into the historical data and used to train the models and machine learning / artificial intelligence processes so that future network incidents may be more rapidly diagnosed and resolved.

Problems solved by technology

However, the expanded capabilities of such networks due to these advancements are not without drawbacks.

For example, networks have become increasingly complex, often incorporating many different types of devices, topologies, communication protocols, and hardware from different vendors, which has made it more difficult to diagnose and resolve any network incidents (e.g., faults, errors, loss of services, and the like).

When these network incidents occur, services provided by the network(s) may be unavailable or function improperly, creating problems for the users and devices supported by the network(s).

While existing technologies provide mechanisms to detect network incidents when they occur, handling the large number of alarms raised by network incidents in today's complex networks presents significant challenges with respect to determining the causes of each network incident and how each incident should be resolved.

Presently available techniques often rely on excessive manpower (e.g., allocating many individuals to diagnose and resolve network incidents) and static knowledge databases to determine how to resolve network incidents and ultimately implement the determined solution(s) for each individual alarm.

Often the overall process consumes long durations of time in order to resolve each network incident.

The problems described above present significant challenges with many newer network technologies, such as 5G networks.

Legacy solutions for resolving such network incidents may lead to severe service degradations and take significant amounts of time to resolve.

One reason more noise may occur in next generation networks is that those networks may rely more on virtualization of network functionality and services as compared to previous networks.

This increased noise may make it more difficult to diagnose and take corrective actions when network incidents occur.

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