Ai based operator install advisor

Patent metadata

Field	Value
Publication number	US-2024311118-A1
Application number	US-202318183127-A
Country	US
Kind code	A1
Filing date	Mar 13, 2023
Priority date	Mar 13, 2023
Publication date	Sep 19, 2024
Grant date	—

Abstract

A computer-implemented method of determining installation compatibility includes identifying one or more entities of an uninstalled operator. The identified one or more entities of the uninstalled operator are parsed and information is extracted from the one or more entities. An existing operator installed on a target container cluster is parsed and information extracted from the entities of the existing operator. The extracted information from the uninstalled operator is compared with the extracted information from the existing operator. A disruption risk to operation of the target container cluster is ranked based on the comparing of the extracted information of the uninstalled operator with the extracted information of the existing operation.

First claim

What is claimed is:

1 . A computer-implemented method of installation and update compatibility, the method comprising:
identifying one or more entities of an uninstalled operator; parsing the identified one or more entities of the uninstalled operator and extracting information from the one or more entities; parsing entities of an existing operator installed on a target container cluster and extracting information from the entities of the existing operator; comparing the extracted information from the uninstalled operator with the extracted information from the existing operator; and ranking a disruption risk to operation of the target container cluster according to a scale based on the comparing of the extracted information of the uninstalled operator with the extracted information of the existing operation.

2 . The computer-implemented method according to claim 1 , further comprising providing an install risk score for the target container cluster.

3 . The computer-implemented method according to claim 1 , wherein the parsing of entities of an existing operator installed on a target container cluster includes querying a current cluster state of an environment of the target container, and performing analysis of at least one existing service, a service account, a role binding, or a cluster binding.

4 . The computer-implemented method according to claim 1 , further comprising providing an Artificial Intelligence (AI) advisor configured to use Natural Language Processing (NLP) to identify the one or more entities of the uninstalled operator.

5 . The computer-implemented method according to claim 4 , wherein the NLP parses the extracted information of the one or more entities into sections.

6 . The computer-implemented method according to claim 5 , wherein the sections include one or more of supported features, a documentation, one or more prerequisites, an operator compatibility level, and a date of publication of the uninstalled operator.

7 . The computer-implemented method according to claim 1 , wherein the identifying of the one or more entities of the uninstalled operator is performed by a Natural Language Processing (NLP) model.

8 . The computer-implemented method according to claim 7 , further comprising training the NLP model to extract information of the one or more entities from a data source.

9 . The computer-implemented method according to claim 7 , further comprising training the NLP model to extract information of the one or more entities from an OperatorHub data source.

10 . The computer-implemented method according to claim 9 , wherein the training of the NLP model includes training a classifier on a custom data set to provide classification over each comparison of the extracted information from the uninstalled operator with the extracted information from the existing operator.

11 . The computer-implemented method according to claim 1 , wherein the ranking of the disruption risk is performed for operation of a Kubernetes target container cluster.

12 . The computer-implemented method according to claim 1 , wherein the ranking of the disruption risk is performed for operation of an Openshift target container cluster.

13 . A computer program product comprising:
one or more computer-readable storage devices and program instructions stored on at least one of the one or more computer-readable storage devices, the program instructions executable by a processor, the program instructions comprising:
program instructions to identify one or more entities of an uninstalled operator;
program instructions to parse the identified one or more entities of the uninstalled operator and extracting information from the one or more entities;
program instructions to parse entities of an existing operator installed on a target container cluster and extract information from the entities of the existing operator;
program instructions to compare the extracted information from the uninstalled operator with the extracted information from the existing operator; and
program instructions to rank a disruption risk to operation of the target container cluster based on the comparing of the extracted information of the uninstalled operator with the extracted information of the existing operation.

14 . The computer program product according to claim 13 , further comprising additional program instructions to perform an additional act comprising providing an install risk score for the target container cluster, wherein the target container cluster comprises a Kubernetes target container cluster or an Openshift target container cluster.

15 . The computer program product according to claim 13 , further comprising additional program instructions to perform additional acts comprising:
providing an Artificial Intelligence (AI) advisor configured to use Natural Language Processing (NLP) to identify the one or more entities of the uninstalled operator; and parsing, by the NLP, the extracted information of the one or more entities into sections, wherein the sections include one or more of supported features, documentation, prerequisites, operator compatibility level, and a date of publication of the uninstalled operator.

16 . The computer program product according to claim 13 , further comprising a Natural Language Processing (NLP) model trained from a data source to identify the one or more entities of the uninstalled operator.

17 . The computer program product according to claim 16 , wherein the NLP model is trained to extract information of the one or more entities of the uninstalled operator from an OperatorHub data source.

18 . The computer program product according to claim 16 , wherein the training of the NLP model includes a classifier trained on a custom data set to provide classification for each comparison of the extracted information from the uninstalled operator with the extracted information from the existing operator.

19 . A computer-implemented method of determining installation compatibility, the method comprising:
detecting a selection on a display of an advisor button operative to provide a risk score analysis of an uninstalled operator on a target container cluster; performing an analysis of the uninstalled operator including querying a current cluster state of the target container cluster, a comparison of the uninstalled operator to current container environmental variables of installed operators, and an assessment of a potential outcome of an installation of the uninstalled operator with an aggregated risk assessment; and providing an install option on the display to proceed with the installation of the uninstalled operator in response to determining that the assessment of the potential outcome is successful based on the aggregated risk assessment.

20 . The computer-implemented method according to claim 19 , wherein:
the aggregated risk assessment comprises a numerical score or a descriptive term; and providing the install option includes displaying a visual indication of the numerical score or descriptive term associated with the aggregated risk assessment.

Assignees

• IBM

Inventors

• Malvankar Abhishek
• Ganci Jr Jr John M
• Wolfe Brent
• Fonseca Carlos A
• Traore Abdoulaye K

Classifications

• G06F8/61 Primary

  Installation · CPC title

• G06N20/00 Primary

  Machine learning · CPC title