Method and system for intelligent cloud planning and decommissioning

• US 10,255,081 B2
• Filed: 07/17/2016
• Issued: 04/09/2019
• Est. Priority Date: 07/17/2015
• Status: Active Grant

• Current Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Original Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Inventors: Bijani, Pramod, Sachdev, Ravi, Bandkar, Mahesh, Gopinath, Ashok, Parulkar, Anand Govind
• Primary Examiner(s): Springer, James E
• Application Number:
  US15/212,285
• Publication Number:
  US 20170017505A1
• Time in Patent Office: 996 Days
• Field of Search: 709226
• CPC Class Codes: G06F 9/44505 Configuring for program initiating, e.g. using registry, configuration files
• CPC Class Codes: G06F 9/44594 Unloading
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance

Abstract

A method for decommissioning an application operating on a computer system or a computer system includes receiving, at a decommissioning system, information that specifies a server to analyze. The decommissioning system determines one or more applications operating on the server, one or more instruction code libraries that are being utilized by the one or more applications, and hardware dependencies of the one or more instruction code libraries. Based on the determined hardware dependencies, the decommissioning system determines whether the application is suitable for migration to a cloud computing system. The decommissioning system generates a report indicating a suitability for migration of the application to the cloud computer system.

Claims

1. A method for decommissioning an application operating on a computer system, the method comprising:

receiving, at a decommissioning system, information that specifies a server to analyze;

determining, by the decommissioning system, a target application operating on the server;

determining, by machine learning logic, one or more applications deployed in a cloud computing system that are related to the target application based on properties of the one or more applications, wherein each of the one or more applications is associated with previously determined cloud deployment information that is stored in training data for training the machine learning logic to make the determination;

when the relatedness between at least one of the plurality of applications and the target application exceeds a threshold, determining that the target application is related to the at least one application of the plurality of applications; and

generating a recommendation report to recommend that the target application be deployed to a cloud computing system according to the cloud deployment information associated with the at least one application of the plurality of applications, wherein the recommendation includes a schematic representation of a recommended deployment topology and a recommended deployment type that corresponds to one of a software as a service (SAAS), platform as a service (PAAS), and infrastructure as a service (IAAS) deployment type;

subsequently receiving a user selected deployment type; and

when the user selected deployment type does not correspond to the recommended deployment type, updating the training data to associate the target application with the selected deployment type.

2. The method according to claim 1, further comprising:

receiving information that defines a business value associated with the target application, wherein if the business value is below a threshold and if the target application is determined to not be suitable for migration to the cloud computing system, indicating on the report that the application should be decommissioned.

3. The method according to claim 1, wherein determining a target application operating on the server further comprises:

installing a discovery tool on the server; and

executing the discovery tool, wherein the discovery tool is configured to identify applications operating on the server, and for each application, determine one or more instruction code libraries that are being utilized by the identified applications; and

determine hardware dependencies of the identified applications.

4. The method according to claim 1, wherein determining whether the application is suitable for migration to a cloud computing system further comprises:

determining one or more of;

a number of users of the application, an amount of usage of the application, and hardware resources utilized by the application.

5. The method according to claim 1, wherein updating the training data further comprises:

associating the properties of the target application with the cloud deployment information selected by the user.

6. The method according to claim 1, wherein the cloud deployment information in the recommendation specifies a cloud service provider.

7. The method according to claim 6, wherein the cloud deployment information in the recommendation specifies a cloud computing template to be utilized when deploying an application to the cloud service provider.

8. The method according to claim 7, further comprising generating, by the decommissioning system, a cloud computing service using the specified cloud computing template with the specified cloud service, wherein after generation of the selected cloud computing service the application to be decommissioned is transferable to one or more data stores of the generated cloud computing service.

9. A system for decommissioning an application operating on a computer system or a computer system, the system comprising:

non-transitory computer readable media with instruction code; and

a processor, wherein based on the instruction code, the processor is configured to;

receive information that specifies a server to analyze;

determine a target application operating on the server;

implement machine learning logic to determine one or more applications deployed in a cloud computing system that are related to the target application based on properties of one or more applications, wherein each of the one or more applications is associated with previously determined cloud deployment information that is stored in training data for training the machine learning logic to make the determination;

when the relatedness between at least one of the plurality of applications and the target application exceeds a threshold, determine that the target application is related to the at least one application of the plurality of applications; and

generate a recommendation report to recommend that the target application be deployed to a cloud computing system according to the cloud deployment information associated with the at least one application of the plurality of applications, wherein the recommendation includes a schematic representation of a recommended deployment topology and a recommended deployment type that corresponds to one of a software as a service (SAAS), platform as a service (PAAS), and infrastructure as a service (IAAS) deployment type;

subsequently receive a user selected deployment type; and

when the user selected deployment type does not correspond to the recommended deployment type, update the training data to associate the target application with the selected deployment type.

10. The system according to claim 9, wherein the processor is further configured to:

receive information that defines a business value associated with the application, wherein if the business value is below a threshold and if the application is determined to not be suitable for migration to the cloud computing system, indicating on the report that the application should be decommissioned.

11. The system according to claim 9, wherein in determining one or more applications operating on the server, the processor is further configured to:

install a discovery tool on the server; and

cause the discovery tool to be executed on the server, wherein the discovery is configured to identify applications operating on the server, and for each application, determine one or more instruction code libraries that are being utilized by the identified applications; and

determine hardware dependencies of the identified applications.

12. The system according to claim 9, wherein in determining whether the application is suitable for migration to a cloud computing system, the processor is further configured to:

determine one or more of;

a number of users of the application, an amount of usage of the application, and hardware resources utilized by the application.

13. The system according to claim 9, wherein the processor is configured to:

associate the properties of the target application with the cloud deployment information selected by the user to thereby update the training data.

14. The system according to claim 9, wherein the cloud deployment information in the recommendation specifies a cloud service provider.

15. The system according to claim 14, wherein the cloud deployment information in the recommendation specifies a cloud computing template to be utilized when deploying an application to the cloud service provider.

16. The system according to claim 15, wherein the processor is further configured to generate a cloud computing service using the specified cloud computing template with the specified cloud service, wherein after generation of the selected cloud computing service the application to be decommissioned is transferable to one or more data stores of the generated cloud computing service.

17. A non-transitory computer readable medium having instruction code stored thereon for decommissioning an application operating on a computer system or a computer system, the instruction code being executable by a machine for causing the machine to perform acts comprising:

receiving information that specifies a server to analyze;

determining a target application operating on the server;

determining, by machine learning logic, one or more applications deployed in a cloud computing system that are related to the target application based on properties of the one or more applications, wherein each of the one or more applications is associated with previously determined cloud deployment information that is stored in training data for training the machine learning logic to make the determination;

when the relatedness between at least one of the plurality of applications and the target application exceeds a threshold, determining that the at least one application is related to the at least one application of the plurality of applications; and

generating a recommendation report to recommend that the target application be deployed to a cloud computing system according to the cloud deployment information associated with the at least one application of the plurality of applications, wherein the recommendation includes a schematic representation of a recommended deployment topology and a recommended deployment type that corresponds to one of a software as a service (SAAS), platform as a service (PAAS), and infrastructure as a service (IAAS) deployment type;

subsequently receiving a user selected deployment type; and

when the user selected deployment type does not correspond to the recommended deployment type, updating the training data to associate the target application with the selected deployment type.

18. The non-transitory computer readable medium according to claim 17, wherein the instruction code is further executable to cause the machine to perform acts comprising:

receiving information that defines a business value associated with the application, wherein if the business value is below a threshold and if the application is determined to not be suitable for migration to the cloud computing system, indicating on the report that the application should be decommissioned.

19. The non-transitory computer readable medium according to claim 17, wherein in determining one or more applications operating on the server, the instruction code is further executable to cause the machine to perform acts comprising further comprises:

installing a discovery tool on the server; and

executing the discovery tool, wherein the discovery tool is configured to identify applications operating on the server, and for each application, determine one or more instruction code libraries that are being utilized by the identified applications; and

determine hardware dependencies of the identified applications.

20. The non-transitory computer readable medium according to claim 17, wherein in determining whether the application is suitable for migration to a cloud computing system, the instruction code is further executable to cause the machine to perform acts comprising:

determining one or more of;

a number of users of the application, an amount of usage of the application, and hardware resources utilized by the application.

21. The non-transitory computer readable medium according to claim 17, wherein the instruction code is further executable to cause the machine to perform acts comprising:

associating the properties of the target application with the cloud deployment information selected by the user to thereby update the training data.

22. The non-transitory computer readable medium according to claim 17, wherein the cloud deployment information in the recommendation specifies a cloud service provider.

23. The non-transitory computer readable medium according to claim 22, wherein the cloud deployment information in the recommendation specifies a cloud computing template to be utilized when deploying an application to the cloud service provider.

24. The non-transitory computer readable medium according to claim 23, wherein the instruction code is further executable to cause the machine to perform acts comprising generating a cloud computing service using the specified cloud computing template with the specified cloud service, wherein after generation of the selected cloud computing service the application to be decommissioned is transferable to one or more data stores of the generated cloud computing service.