DYNAMICALLY QUANTIFYING THE DEMAND OF EACH SOFTWARE COMPONENT OF EACH SOFTWARE STACK DEPLOYED IN THE CLOUD ENVIRONMENT

• US 20140149421A1
• Filed: 11/27/2012
• Published: 05/29/2014
• Est. Priority Date: 11/27/2012
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Ashok, Rohith K., Ims, Steven D., Kochuba, James K., Quirk, Aaron J.
• Granted Patent: US 9,053,446 B2
• Time in Patent Office: Days
• US Class Current: 707/741
• CPC Class Codes: G06F 2209/5019 Workload prediction
• CPC Class Codes: G06F 9/5072 Grid computing
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations

Abstract

A method, system and computer program product for dynamically quantifying a demand for the software components deployed in a cloud environment. An administrative server generates a table mapping the software levels of the software components of the deployed software stacks with the number of instances of the deployed software stacks tracked over a period of time. The depth weight based on subtracting a depth index (zero-based) from a depth size is calculated for each software component, where a depth index refers to the software level of the software component in question and a depth size refers to the number of software levels for the software stack bearing the software component in question. A metric used in quantifying the demand for the software component (“popularity index”) is then determined for each software component based on the number of deployed instances and the depth weight for that software component.

Claims

1. A method for dynamically quantifying a demand for software components deployed in a cloud environment, the method comprising:

tracking a number of instances that each of a plurality of software stacks is deployed in said cloud environment over a period of time;

generating a table mapping software levels of software components for each of said plurality of software stacks to said tracked number of instances that each of said plurality of software stacks is deployed in said cloud environment over said period of time;

identifying a number of deployed instances for each of said software components based on said table;

calculating a weight for each of said software components; and

determining, by processor, a popularity index for each of said software components based on said number of deployed instances and said weight.

2. The method as recited in claim 1 further comprising:

provisioning resources in said cloud environment based on said popularity index for each of said software components.

3. The method as recited in claim 1, wherein said weight is based on subtracting a depth index from a depth size, wherein said depth index corresponds to a software level, wherein said depth size corresponds to a number of software levels of an associated software stack.

4. The method as recited in claim 3, wherein said popularity index for a software component is equal to the equation:

P=R*(Ds−

Di);

wherein said P is said popularity index for said software component, wherein said R is a number of deployed instances for said software component, wherein said Ds is said depth size of a software stack including said software component, and wherein said Di is said depth index for said software component.

5. The method as recited in claim 3, wherein each of said software components of each of said plurality of software stacks comprises a type of one of the following:

an operating system, a middleware application and application binaries.

6. The method as recited in claim 5, wherein said weight of said operating system is higher than said weight of said middleware application, wherein said weight of said middleware application is higher than said weight of said application binaries.

7. The method as recited in claim 1, wherein said number of deployed instances for each of said software components is identified across said plurality of software stacks deployed in said cloud environment over said period of time.

8. A computer program product embodied in a computer readable storage medium for dynamically quantifying a demand for software components deployed in a cloud environment, the computer program product comprising the programming instructions for:

tracking a number of instances that each of a plurality of software stacks is deployed in said cloud environment over a period of time;

generating a table mapping software levels of software components for each of said plurality of software stacks to said tracked number of instances that each of said plurality of software stacks is deployed in said cloud environment over said period of time;

identifying a number of deployed instances for each of said software components based on said table;

calculating a weight for each of said software components; and

determining a popularity index for each of said software components based on said number of deployed instances and said weight.

9. The computer program product as recited in claim 8 further comprising the programming instructions for:

provisioning resources in said cloud environment based on said popularity index for each of said software components.

10. The computer program product as recited in claim 8, wherein said weight is based on subtracting a depth index from a depth size, wherein said depth index corresponds to a software level, wherein said depth size corresponds to a number of software levels of an associated software stack.

11. The computer program product as recited in claim 10, wherein said popularity index for a software component mapped in said table is equal to the equation:

P=R*(Ds−

Di);

wherein said P is said popularity index for said software component, wherein said R is a number of deployed instances for said software component, wherein said Ds is said depth size of a software stack including said software component, and wherein said Di is said depth index for said software component.

12. The computer program product as recited in claim 10, wherein each of said software components of each of said plurality of software stacks comprises a type of one of the following:

an operating system, a middleware application and application binaries.

13. The computer program product as recited in claim 12, wherein said weight of said operating system is higher than said weight of said middleware application, wherein said weight of said middleware application is higher than said weight of said application binaries.

14. The computer program product as recited in claim 8, wherein said number of deployed instances for each of said software components mapped in said table is identified across said plurality of software stacks deployed in said cloud environment over said period of time.

15. A system, comprising:

a memory unit for storing a computer program for dynamically quantifying a demand for software components deployed in a cloud environment; and

a processor coupled to said memory unit, wherein said processor, responsive to said computer program, comprises;

circuitry for tracking a number of instances that each of a plurality of software stacks is deployed in said cloud environment over a period of time;

circuitry for generating a table mapping software levels of software components for each of said plurality of software stacks to said tracked number of instances that each of said plurality of software stacks is deployed in said cloud environment over said period of time;

circuitry for identifying a number of deployed instances for each of said software components based on said table;

circuitry for calculating a weight for each of said software components; and

circuitry for determining a popularity index for each of said software components based on said number of deployed instances and said weight.

16. The system as recited in claim 15, wherein said processor further comprises:

circuitry for provisioning resources in said cloud environment based on said popularity index for each of said software components.

17. The system as recited in claim 15, wherein said weight is based on subtracting a depth index from a depth size, wherein said depth index corresponds to a software level, wherein said depth size corresponds to a number of software levels of an associated software stack.

18. The system as recited in claim 17, wherein said popularity index for a software component mapped in said table is equal to the equation:

P=R*(Ds−

Di);

wherein said P is said popularity index for said software component, wherein said R is a number of deployed instances for said software component, wherein said Ds is said depth size of a software stack including said software component, and wherein said Di is said depth index for said software component.

19. The system as recited in claim 17, wherein each of said software components of each of said plurality of software stacks comprises a type of one of the following:

an operating system, a middleware application and application binaries.

20. The system as recited in claim 19, wherein said weight of said operating system is higher than said weight of said middleware application, wherein said weight of said middleware application is higher than said weight of said application binaries.