View source for 18523569. HYPOTHESIS DRIVEN DIAGNOSIS OF NETWORK SYSTEMS simplified abstract (Juniper Networks, Inc.)

=HYPOTHESIS DRIVEN DIAGNOSIS OF NETWORK SYSTEMS=


==Organization Name==

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


[[Category:Juniper Networks, Inc.]]

==Inventor(s)==

[[:Category:Gert Grammel of Ditzingen (DE)|Gert Grammel of Ditzingen (DE)]][[Category:Gert Grammel of Ditzingen (DE)]]

[[:Category:Jayanthi R of Bangalore (IN)|Jayanthi R of Bangalore (IN)]][[Category:Jayanthi R of Bangalore (IN)]]

[[:Category:Chandrasekhar A of Bangalore (IN)|Chandrasekhar A of Bangalore (IN)]][[Category:Chandrasekhar A of Bangalore (IN)]]

==HYPOTHESIS DRIVEN DIAGNOSIS OF NETWORK SYSTEMS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18523569 titled 'HYPOTHESIS DRIVEN DIAGNOSIS OF NETWORK SYSTEMS

==Simplified Explanation==

The abstract describes a method for identifying root causes of faults in a network by utilizing Bayesian modeling based on resource and event dependencies. 

* The method involves obtaining data indicating resource dependencies and event dependencies in a network.
* A Bayesian model is generated based on resource types and event types.
* When a fault is indicated in the network, fault data is collected and used to generate root cause hypotheses.
* The root cause hypotheses are ordered based on probabilities associated with each hypothesis.
* The ordered root cause hypotheses are then outputted for further analysis.

== Potential Applications ==
This technology could be applied in various industries such as telecommunications, IT infrastructure management, and network security to quickly identify and resolve faults in networks.

== Problems Solved ==
This technology helps in efficiently identifying the root causes of faults in complex networks, reducing downtime and improving overall network performance.

== Benefits ==
The benefits of this technology include faster fault resolution, improved network reliability, and enhanced decision-making based on accurate root cause analysis.

== Potential Commercial Applications ==
Potential commercial applications of this technology include network monitoring and management software, fault detection and analysis tools, and network troubleshooting services.

== Possible Prior Art ==
One possible prior art for this technology could be fault detection and root cause analysis methods used in network management systems.

=== Unanswered Questions ===

=== How does this technology compare to traditional fault detection methods in terms of accuracy and efficiency? ===
This article does not provide a direct comparison between this technology and traditional fault detection methods. Further research or testing would be needed to determine the effectiveness of this technology compared to existing methods.

=== What are the limitations of this technology in identifying complex or intermittent faults in networks? ===
The article does not address the limitations of this technology in identifying complex or intermittent faults. It would be important to understand the scope and capabilities of this technology in handling such scenarios.


==Original Abstract Submitted==

An example method includes obtaining, by one or more processors, data indicating resource dependencies between a plurality of resources in a network and event dependencies between a plurality of network events and one or more of the plurality of resources; generating a Bayesian model based on resource types of the plurality of resources and event types of the plurality of network events; receiving an indication of a fault in the network; collecting fault data and generating, based on the Bayesian model and the fault data, a plurality of root cause hypotheses for the fault; ordering the plurality of root cause hypotheses based on respective root cause probabilities associated with the plurality of root cause hypotheses; and outputting the ordered plurality of root cause hypotheses.



[[Category:H04L41/0631]]
[[Category:H04L41/069]]