View source for 18527471. IDENTIFYING ROOT CAUSE OF FAILURES THROUGH DETECTION OF NETWORK SCOPE FAILURES simplified abstract (Juniper Networks, Inc.)

=IDENTIFYING ROOT CAUSE OF FAILURES THROUGH DETECTION OF NETWORK SCOPE FAILURES=


==Organization Name==

[[:Category:Juniper Networks, Inc.|Juniper Networks, Inc.]]


[[Category:Juniper Networks, Inc.]]

==Inventor(s)==

[[:Category:Jing Cheng of San Jose CA (US)|Jing Cheng of San Jose CA (US)]][[Category:Jing Cheng of San Jose CA (US)]]

[[:Category:Jisheng Wang of Palo Alto CA (US)|Jisheng Wang of Palo Alto CA (US)]][[Category:Jisheng Wang of Palo Alto CA (US)]]

[[:Category:Kush Shah of Santa Clara CA (US)|Kush Shah of Santa Clara CA (US)]][[Category:Kush Shah of Santa Clara CA (US)]]

==IDENTIFYING ROOT CAUSE OF FAILURES THROUGH DETECTION OF NETWORK SCOPE FAILURES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18527471 titled 'IDENTIFYING ROOT CAUSE OF FAILURES THROUGH DETECTION OF NETWORK SCOPE FAILURES

==Simplified Explanation==

The patent application describes techniques for a network management system to identify root cause failures by detecting network scope failures using a hierarchical attribution graph and machine learning models.

* The NMS comprises one or more processors and a memory with instructions for generating a hierarchical attribution graph representing different network scopes, receiving network event data indicating operational behavior, and applying a machine learning model to detect failures in specific network scopes.

== Potential Applications ==
This technology could be applied in various industries where network management is crucial, such as telecommunications, IT services, and data centers.

== Problems Solved ==
1. Efficiently identifying root cause failures in network systems.
2. Improving network performance and reliability by quickly detecting and resolving failures.

== Benefits ==
1. Minimizing downtime by promptly addressing network failures.
2. Enhancing overall network efficiency and performance.
3. Streamlining network management processes through automation.

== Potential Commercial Applications ==
Optimizing network performance and reliability in telecommunications companies.
Improving IT services by quickly identifying and resolving network failures.

== Possible Prior Art ==
One possible prior art could be existing network management systems that use machine learning models for fault detection and root cause analysis in network systems.

== Unanswered Questions ==
=== How does this technology compare to existing network management systems in terms of accuracy and efficiency in failure detection? ===
The article does not provide a direct comparison between this technology and existing systems, so it is unclear how it performs in relation to them.

=== What are the potential limitations or challenges in implementing this technology in real-world network environments? ===
The article does not address any potential obstacles or challenges that may arise when implementing this technology in practical network settings.


==Original Abstract Submitted==

Techniques are described by which a network management system (NMS) is configured to provide identification of root cause failure through the detection of network scope failures. For example, the NMS comprises one or more processors; and a memory comprising instructions that when executed by the one or more processors cause the one or more processors to: generate a hierarchical attribution graph comprising attributes representing different network scopes at different hierarchical levels; receive network event data, wherein the network event data is indicative of operational behavior of the network, including one or more of successful events or one or more failure events associated with one or more client devices; and apply a machine learning model to the network event data and to a particular network scope in the hierarchical attribution graph to detect whether the particular network scope has failure.



[[Category:H04L41/0631]]
[[Category:H04W24/04]]