Orchestration engine resources and blueprint definitions for hybrid cloud composition

Patent metadata

Field	Value
Publication number	US-11487590-B2
Application number	US-201816155109-A
Country	US
Kind code	B2
Filing date	Oct 9, 2018
Priority date	Oct 9, 2018
Publication date	Nov 1, 2022
Grant date	Nov 1, 2022

Abstract

Techniques that facilitate orchestration engine resources and/or blueprint definitions for hybrid cloud composition are provided. In one example, a system includes a blueprint component and a blueprint transformation component. The blueprint component determines one or more abstract resource types for an abstract blueprint associated with a computing platform. The one or more abstract resource types are indicative of information associated with one or more computing resources for the computing platform. The blueprint transformation component transforms the one or more abstract resource types for the abstract blueprint into one or more executable resources for an executable blueprint that is executable by an orchestration engine.

First claim

What is claimed is:

1. A system, comprising:
a memory that stores computer executable components;
a processor that executes computer executable components stored in the memory, wherein the computer executable components comprise:
a blueprint component that determines, using machine learning that monitors a computing platform to learn one or more features related to one or more abstract resource types, a plurality of abstract resource types for an abstract blueprint associated with the computing platform, wherein the plurality of abstract resource types are indicative of information associated with a plurality of computing resources for the computing platform, and the plurality of abstract resource types comprises at least two of virtual machine data, database data, backup as a service data, or operating system monitoring data; and
a blueprint transformation component that transforms, based on transformation criteria, the plurality of abstract resource types for the abstract blueprint into one or more executable resources for an executable blueprint that is executable by an orchestration engine, wherein the one or more executable resources are translated into hardware and software requirements for the computing platform, and wherein the transformation criteria comprises a risk level criterion and an estimated benefit criterion.

2. The system of claim 1 , wherein the executable blueprint is indicative of a machine-readable representation of the one or more executable resources.

3. The system of claim 1 , wherein the blueprint transformation component generates mapping data indicative of a mapping of the plurality of abstract resource types to the one or more executable resources, and wherein the blueprint transformation component generates the executable blueprint based on the mapping data.

4. The system of claim 3 , wherein the transformation criteria further comprises at least one of a compliance criteria, a cost criteria, or a geography criteria.

5. The system of claim 3 , wherein the blueprint transformation component generates the mapping data based on at least one of service level agreement data, software data, deployment environment data, cost data, security data, response time data, dependency data, deadline data, user data, historical data, performance data, risk level data, and estimated benefit data.

6. The system of claim 1 , wherein the one or more executable resources comprises executable data for at least one of a virtual machine computing environment, a database computing environment, a backup service computing environment, and an operating system monitoring environment.

7. The system of claim 1 , wherein the blueprint component generates the plurality of abstract resource types for a hybrid cloud-based computing platform, a public cloud-based computing platform, or a private cloud-based computing platform.

8. The system of claim 1 , wherein the blueprint transformation component transforms the plurality of abstract resource types for the abstract blueprint into one or more executable resources for the executable blueprint to facilitate improved performance for the computing platform.

9. A computer-implemented method, comprising:
determining, by a system operatively coupled to a processor, using machine learning that monitors a computing platform to learn one or more features related to one or more abstract resource types, a plurality of abstract resource types for an abstract blueprint associated with information for one or more computing resources of the computing platform, and the plurality of abstract resource types comprises at least two of virtual machine data, database data, backup as a service data, or operating system monitoring data;
transforming, by the system, based on transformation criteria, the plurality of abstract resource types for the abstract blueprint into one or more executable resources for an executable blueprint, wherein the transformation criteria comprises a risk level criterion and an estimated benefit criterion; and
executing, by the system, the executable blueprint on an orchestration engine associated with the computing platform to specify hardware and software requirements for the computing platform.

10. The computer-implemented method of claim 9 , further comprising:
generating, by the system, mapping data indicative of a mapping of the plurality of abstract resource types to the one or more executable resources.

11. The computer-implemented method of claim 10 , further comprising:
generating, by the system, the executable blueprint based on the mapping data.

12. The computer-implemented method of claim 10 , wherein the transformation criteria further comprises at least one of a compliance criteria, a cost criteria, or a geography criteria.

13. The computer-implemented method of claim 10 , further comprising:
generating, by the system, the mapping data based on at least one of service level agreement data, software data, deployment environment data, cost data, security data, response time data, dependency data, deadline data, user data, historical data, performance data, risk level data, and estimated benefit data.

14. The computer-implemented method of claim 10 , wherein the executing the executable blueprint comprises improving performance of the computing platform.

15. The computer-implemented method of claim 9 , wherein the executable blueprint is indicative of a machine-readable representation of the one or more executable resources.

16. A computer program product facilitating an orchestration engine process, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:
determine, by the processor, using machine learning that monitors a computing platform to learn one or more features related to one or more abstract resource types, a plurality of abstract resource types for an abstract blueprint associated with information for one or more computing resources of the computing platform, and the plurality of abstract resource types comprises at least two of virtual machine data, database data, backup as a service data, or operating system monitoring data;
transform, by the processor, based on transformation criteria, the plurality of abstract resource types for the abstract blueprint into one or more executable resources for an executable blueprint, wherein the transformation criteria comprises a risk level criterion and an estimated benefit criterion; and
execute, by the processor, the executable blueprint on the computing platform to specify hardware and software requirements for the computing platform.

17. The computer program product of claim 16 , wherein the program instructions are further executable by the processor to cause the processor to:
generate, by the processor, mapping data indicative of a mapping of the plurality of abstract resource types to the one or more executable resources.

18. The computer program product of claim 17 , wherein the program instructions are further executable by the processor to cause the processor to:
generate, by the processor, the executable blueprint based on the mapping data.

19. The computer program product of claim 17 , wherein the transformation criteria further comprises at least one of a compliance criteria, a cost criteria, or a geography criteria.

20. The computer program product of claim 17 , wherein the program instructions are further executable by the processor to caus

Assignees

• Kyndryl Inc

Inventors

• Asthana Neeraj
• Chefalas Thomas E
• Karve Alexei
• Tayade Ameya

Classifications

• G06F9/5072 Primary

  Grid computing · CPC title

• G06F9/5077 Primary

  Logical partitioning of resources; Management or configuration of virtualized resources (specific details on emulation or internal functioning of virtual machines G06F9/455) · CPC title

• G06N20/00

  Machine learning · CPC title