Automated application deployment in a managed services domain

Patent metadata

Field	Value
Publication number	US-11108655-B2
Application number	US-201816029502-A
Country	US
Kind code	B2
Filing date	Jul 6, 2018
Priority date	Jul 6, 2018
Publication date	Aug 31, 2021
Grant date	Aug 31, 2021

Abstract

Techniques are described relating to automated deployment of an application in a managed services domain of a cloud computing environment. One or more of such techniques may minimize human interaction or intervention during deployment of the application. An associated method includes receiving from a client system a request to deploy the application in a target environment and analyzing the request via a machine learning knowledge model. Additionally, the method includes requesting from the client system access to the target environment and, upon receiving access to the target environment, validating the target environment through inspection. Upon validating the target environment, the method further includes facilitating presentation of an application deployment plan through an interface of the client system. Responsive to client approval of the application deployment plan, the method further includes deploying the application by facilitating application installation in the target environment via the machine learning knowledge model.

First claim

What is claimed is:

1. A method of automated deployment of an application among a plurality of applications in a managed services domain, the method comprising:
receiving from a client system a deployment request with respect to a target environment;
analyzing the deployment request via a machine learning knowledge model, wherein the machine learning knowledge model determines, based upon application configuration details included in the deployment request, installation prerequisites or dependent components by employing one or more heuristics having predetermined input that includes installation prerequisites or dependent components of a most recently encountered deployment request;
requesting from the client system access to the target environment;
upon receiving access to the target environment, validating the target environment through inspection;
upon validating the target environment, facilitating presentation of an application deployment plan through an interface of the client system; and
responsive to client approval of the application deployment plan, deploying the application by facilitating application installation in the target environment via the machine learning knowledge model.

2. The method of claim 1 , further comprising:
addressing at least one issue pertaining to the application installation.

3. The method of claim 2 , wherein addressing the at least one issue pertaining to the application installation comprises:
applying artificial intelligence techniques of the machine learning knowledge model to seek a resolution to the at least one issue; and
responsive to failing to determine a resolution to the at least one issue through application of the artificial intelligence techniques, receiving from domain administration a manual override of the application installation to address the at least one issue, wherein the manual override includes feedback.

4. The method of claim 3 , wherein addressing the at least one issue pertaining to the application installation further comprises:
submitting the feedback to at least one subject matter expert among a plurality of subject matter experts in the managed services domain for validation; and
upon receiving validation of the feedback, adapting a knowledge base of the machine learning knowledge model based upon the feedback.

5. The method of claim 1 , wherein validating the target environment comprises:
responsive to determining that specifications of the target environment do not meet minimum requirements for deployment of the application, notifying the client system of at least one upgrade necessary to meet the minimum requirements.

6. The method of claim 5 , wherein validating the target environment further comprises:
responsive to determining that at least one dependent component required for deployment of the application is missing from the target environment, notifying the client system of the necessity of the at least one dependent component.

7. The method of claim 1 , further comprising:
responsive to client disapproval of the application deployment plan, receiving from the client system at least one proposed alternative.

8. The method of claim 1 , wherein configuring a knowledge base of the machine learning knowledge model comprises:
initializing the knowledge base based upon expert input.

9. The method of claim 8 , wherein initializing the knowledge base comprises:
incorporating respective base models of the plurality of applications into the knowledge base; and
incorporating performance tuning parameters based upon compatibility between respective operating systems and respective aspects of the plurality of applications.

10. The method of claim 8 , wherein configuring the knowledge base further comprises:
extending the knowledge base consequent to at least one update within the managed services domain selected from the group consisting of a system upgrade, an application integration, and an application fix.

11. The method of claim 1 , wherein deploying the application comprises employing one or more heuristics to handle a newly encountered issue by selecting a deployment action based upon a deployment action taken in connection with a most recently encountered issue.

12. The method of claim 1 , wherein deploying the application comprises employing one or more heuristics to handle a newly encountered issue by selecting a deployment action based upon a deployment action taken in connection with a previously encountered issue having a highest degree of similarity to the newly encountered issue.

13. A computer program product comprising a computer readable storage medium having program instructions embodied therewith for automated deployment of an application among a plurality of applications in a managed services domain, the program instructions executable by a computing device to cause the computing device to:
receive from a client system a deployment request with respect to a target environment;
analyze the deployment request via a machine learning knowledge model, wherein the machine learning knowledge model determines, based upon application configuration details included in the deployment request, installation prerequisites or dependent components by employing one or more heuristics having predetermined input that includes installation prerequisites or dependent components of a most recently encountered deployment request;
request from the client system access to the target environment;
upon receiving access to the target environment, validate the target environment through inspection;
upon validating the target environment, facilitate presentation of an application deployment plan through an interface of the client system; and
responsive to client approval of the application deployment plan, deploy the application by facilitating application installation in the target environment via the machine learning knowledge model.

14. The computer program product of claim 13 , wherein the product instructions further cause the computing device to:
address at least one issue pertaining to the application installation.

15. The computer program product of claim 14 , wherein addressing the at least one issue pertaining to the application installation comprises:
applying artificial intelligence techniques of the machine learning knowledge model to seek a resolution to the at least one issue; and
responsive to failing to determine a resolution to the at least one issue through application of the artificial intelligence techniques, receiving from domain administration a manual override of the application installation to address the at least one issue, wherein the manual override includes feedback.

16. The computer program product of claim 15 , wherein addressing the at least one issue pertaining to the application installation further comprises:
submitting the feedback to at least one subject matter expert among a plurality of subject matter experts in the managed services domain for validation; and
upon receiving validation of the feedback, adapting a knowledge base of the machine learning knowledge model based upon the feedback.

17. A system comprising:
a processor; and
a memory storing an application program, which, when executed on the processor, performs an operation of automated deployment of an application among a plurality of applications in a managed services domain, the operation comprising:
receiving from a client system a deployment request with respect to a target environment;
analyzing the deployment request via a machine learning knowledge model, wherein the machine

Assignees

• IBM

Inventors

• Gangadhar Jayanth
• Ramanujam Karthick
• Basavaraj Ullas M
• Shah Ankur B

Classifications

• H04L67/10

  in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46) · CPC title

• H04L41/16

  using machine learning or artificial intelligence · CPC title

• G06F8/60 Primary

  Software deployment · CPC title

• G06N20/00

  Machine learning · CPC title

• H04L41/5054 Primary

  Automatic deployment of services triggered by the service manager, e.g. service implementation by automatic configuration of network components · CPC title