System and method for network root cause analysis

Patent metadata

Field	Value
Publication number	US-10904071-B2
Application number	US-202016816604-A
Country	US
Kind code	B2
Filing date	Mar 12, 2020
Priority date	Oct 27, 2017
Publication date	Jan 26, 2021
Grant date	Jan 26, 2021

Abstract

Disclosed herein is a multi-level analysis for determining a root cause of a network problem by performing a first level of the multi-level process that includes collecting data from one or more network components, generating a set of system metrics where each system metric of the set representing a portion of the data, ranking the set of system metrics based on a level of correlation of each system metric to the network problem to yield a ranked set of system metrics, and providing a visual representation of the first level of the multi-level process. A second level of the multi-level process includes receiving an input identifying one or more of the ranked set of system metrics to be excluded from analysis and performing a conditional analysis using only ones of the set of system metrics that are not identified for exclusion.

First claim

What is claimed is:

1. A method to determine a root cause of a network problem in a network, comprising:
collecting data from one or more network components;
generating a set of system metrics, each system metric of the set representing a portion of the collected data, at least one system metric of the set being a target metric corresponding to the network problem;
ranking the set of system metrics based on a level of correlation of each system metric to the network problem to yield a ranked set of system metrics; and
receiving an input identifying one or more of the highest ranked system metrics of the set of system metrics to be excluded from analysis; and
performing a conditional analysis for determining the root cause of the network problem using only ones of the set of system metrics that are not identified for exclusion.

2. The method of claim 1 , wherein ranking the set of system metrics is performed by using a machine learning model in conjunction with a cross validation or regression technique to determine a correlation level of each one of the set of system metrics to the target metric.

3. The method of claim 1 , wherein the generating the set of system metrics includes grouping the data into one or more sets;
analyzing the one or more sets to identify one or more common characteristics between two or more of the sets; and
combining the two or more of the sets into a single system metric of the set of system metrics.

4. The method of claim 3 , further comprising:
tagging each system metric of the set, the tag being an identifier of underlying data being represented in each system metric.

5. The method of claim 1 , wherein each system metric of the set is a time series representation of corresponding data in the same set.

6. The method of claim 1 , further comprising:
presenting a result of the conditional analysis on a display, the result identifying the root cause.

7. The method of claim 1 , wherein the data in each of the set of system metrics include one or more of a network equipment latency, one or more CPU usages, one or more disk usages, processes running on one or more servers in the network, network traffic and application specific data.

8. A system for to determining a root cause of a network problem in a network, the system comprising:
non-transitory computer readable memory configured to store computer-readable instructions therein; and
one or more processors programmed to cooperate with the computer-readable instructions to perform operations comprising:
collecting data from one or more network components;
generating a set of system metrics, each system metric of the set representing a portion of the collected data, at least one system metric of the set being a target metric corresponding to the network problem;
ranking the set of system metrics based on a level of correlation of each system metric to the network problem to yield a ranked set of system metrics; and
receiving an input identifying one or more of the highest ranked system metrics of the set of system metrics to be excluded from analysis; and
performing a conditional analysis for determining the root cause of the network problem using only ones of the set of system metrics that are not identified for exclusion.

9. The system of claim 8 , wherein the system is a network analytics platform.

10. The system of claim 8 , the operations further comprising:
presenting a result of the conditional analysis;
receiving a further feedback identifying one of the set of system metrics as the root cause of the network problem from among ones of the set of system metrics presented as part of the result of the conditional analysis; and
presenting a recommendation for addressing the network problem.

11. The system of claim 8 , wherein the generating the set of system metrics comprises:
grouping the data into one or more sets;
analyzing the one or more sets to identify one or more common characteristics between two or more of the sets; and
combining the two or more of the sets into a single system metric of the set of system metrics.

12. The system of claim 11 , the operations further comprising:
tagging each system metric of the set, the tag being an identifier of underlying data being represented in each system metric.

13. The system of claim 8 , wherein each system metric of the set is a time series representation of corresponding data in the same set.

14. The system of claim 8 , wherein the data in each of the set of system metrics include one or more of a network equipment latency, one or more CPU usages, one or more disk usages, processes running on one or more servers in the network, network traffic and application specific data.

15. A non-transitory computer-readable media having computer-readable instructions stored therein to determine a root cause of a network problem in a network, which when executed by a processor cause the processor to perform operations comprising:
collecting data from one or more network components;
generating a set of system metrics, each system metric of the set representing a portion of the collected data, at least one system metric of the set being a target metric corresponding to the network problem;
ranking the set of system metrics based on a level of correlation of each system metric to the network problem to yield a ranked set of system metrics; and
receiving an input identifying one or more of the highest ranked system metrics of the set of system metrics to be excluded from analysis; and
performing a conditional analysis for determining the root cause of the network problem using only ones of the set of system metrics that are not identified for exclusion.

16. The non-transitory computer-readable media of claim 15 , wherein ranking the set of system metrics is performed by using a machine learning model in conjunction with a cross validation or regression technique to determine a correlation level of each one of the set of system metrics to the target metric.

17. The non-transitory computer-readable media of claim 15 , the operations further comprising:
presenting a result of the conditional analysis;
receiving a further feedback identifying one of the set of system metrics as the root cause of the network problem from among ones of the set of system metrics presented as part of the result of the conditional analysis; and
presenting a recommendation for addressing the network problem.

18. The non-transitory computer-readable media of claim 15 , wherein the generating the set of system metrics comprises:
grouping the data into one or more sets;
analyzing the one or more sets to identify one or more common characteristics between two or more of the sets; and
combining the two or more of the sets into a single system metric of the set of system metrics.

19. The non-transitory computer-readable media of claim 18 , the operations further comprising:
tagging each system metric of the set, the tag being an identifier of underlying data being represented in each system metric.

20. The non-transitory computer-readable media of claim 15 , wherein each system metric of the set is a time series representation of corresponding data in the same set.

Assignees

• Cisco Tech Inc

Inventors

• Jeyakumar Vimal
• Parandehgheibi Ali
• Sloane Andy
• Kulshreshtha Ashutosh
• Yadav Navindra
• Madani Omid

Classifications

• H04L41/16

  using machine learning or artificial intelligence · CPC title

• H04L41/22

  comprising specially adapted graphical user interfaces [GUI] · CPC title

• H04L41/0631 Primary

  using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis · CPC title

• G06N20/00

  Machine learning · CPC title