Systems and methods for providing decision time brokerage in a hybrid cloud ecosystem

• US 9,967,159 B2
• Filed: 01/31/2013
• Issued: 05/08/2018
• Est. Priority Date: 01/31/2012
• Status: Active Grant

• Current Assignee: Infosys Limited
• Original Assignee: Infosys Limited
• Inventors: Shroff, Abhijit, Donthireddy, Venkata Reddy, Rajangam, Santha Kumar, Sridhar, Prasanna Raman, Jayaraj, Babu
• Primary Examiner(s): ADE, OGER GARCIA
• Application Number:
  US13/755,437
• Publication Number:
  US 20130198050A1
• Time in Patent Office: 1,923 Days
• Field of Search: 705 34
• CPC Class Codes: G06F 9/5072 Grid computing
• CPC Class Codes: H04L 41/5009 Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]
• CPC Class Codes: H04L 43/04 Processing captured monitoring data, e.g. for logfile generation

Abstract

A method and system for providing decision-time brokerage in a hybrid cloud ecosystem is disclosed. Disclosed embodiments may include receiving a workload input by a brokerage engine executing in a computing device communicably connected to at least one cloud computing node in a cloud platform of a cloud service provider, determining resource optimization for the workload input by the brokerage engine, monitoring the workload input for compliance with one or more audit and regulatory metrics, monitoring the cost consumption of the workload input, capturing non-functional context data associated with the workload input into a context repository database, applying one or more rules to the workload, deploying the workload across the one or more cloud platforms. In some embodiments, a deployment recommendation may be provided prior to deployment of the workload. In some embodiments, a social collaboration workflow may be triggered whereby one or more users are provided the deployment recommendation.

Claims

1. A method for improved deployment of workloads across cloud platforms of an integrated cloud ecosystem, the method comprising:

receiving, by a cloud brokerage computing device, workload input data for a workload, wherein the workload input data comprises at least one workload pattern parameter, policy parameter, or cost attribute;

determining, by the cloud brokerage computing device, resource optimization data based on the workload input data, wherein the determining the resource optimization data comprises determining compliance of the workload with one or more audit and regulatory metrics, cost consumption of the workload, and non-functional context data associated with the workload, wherein the audit and regulatory metrics include at least one of an enterprise regulatory compliance metric, an industry standards body compliance metric or a governmental regulatory compliance metric and the cost consumption includes metering cost associated with deploying the workload based on a budget;

applying, by the cloud brokerage computing device, one or more rules to the workload input data, wherein the one or more rules comprise at least one of an information technology, business, compliance, departmental, portfolio or enterprise context rule in a rules repository database; and

deploying, by the cloud brokerage computing device, the workload across a cloud ecosystem according to deployment plan data generated based on the resource optimization data and the application of the one or more rules to the workload input data, wherein the deployment plan data comprises a deployment plan for optimally deploying the workload that is generated prior to deployment of the workload on any platform.

2. The method of claim 1, wherein the non-functional context data comprises at least one of enterprise context data, user persona context data, business workload context data, technical context data, application lifecycle context data, service provider context data, or service usage context data.

3. The method of claim 2, wherein the enterprise context data comprises at least one of information security rules, governance rules, budget data, business goal targets data, capital and operation expenditure data or business performance data.

4. The method of claim 2, wherein the user persona context data comprises at least one of job profile data, user behavior data, user service usage data, organizational hierarchy data, a social connections index or historical usage data.

5. The method of claim 2, wherein the technical context data comprises workload resource requirements data, technical interoperability data, location data or data usage data.

6. The method of claim 2, wherein the service usage context data comprises computing resource consumption data, service usage cost data, or service usage compliance data.

7. The method of claim 1, wherein the determining compliance with one or more audit and regulatory metrics further comprises:

analyzing the non-functional context data associated with the workload for deviations from one or more retrieved policies comprising a predefined industry regulatory compliance policy, a predefined enterprise regulatory compliance policy, or a predefined governmental regulatory compliance policy; and

triggering an alert notification based on the analysis, wherein the alert notification is a communicated to a user through a graphical user interface on a display device.

8. The method of claim 7, further comprising providing a remedial recommendation to a user through the display device, wherein the remedial recommendation comprises information related to enforcing policy compliance of the workload.

9. The method of claim 7, further comprising:

retrieving, by the cloud brokerage computing device, at least one first user profile context data from the context repository database;

creating, by the cloud brokerage computing device, a functional collaboration context on the basis of the at least one first user profile context data, wherein the functional collaboration context associated with the workload is determined based on an organizational hierarchy associated with the at least one first user profile, historical behavior associated with the at least one first user profile, or a social connection context associated with the at least one first user profile; and

communicating, by the cloud brokerage computing device, a collaboration notification to a second computing device associated with at least one second user, wherein the at least one second computing device is network connected to the at least one first user computing device and wherein;

the network address of the at least one second computing device is determined based on historical behavior data associated with at least one second user profile, the at least one second user profile context data being stored on the context repository database; and

the at least one second user profile is determined on the basis of the functional collaboration context associated with the first user profile.

10. The method of claim 9, further comprising communicating, by the cloud brokerage computing device, a deployment approval notification to the second computing device.

11. The method of claim 10, further comprising deploying, by the cloud brokerage computing device, the workload only when the deployment approval notification is relayed back by the second computing device.

12. The method of claim 1, wherein the monitoring cost consumption further comprises:

metering cloud service consumption or computing resource consumption by the workload;

calculating charges associated with the workload on the basis of service subscription data retrieved from a service subscriptions repository database;

validating charges associated with the cloud service consumption or computing resource consumption by the workload against budget data in a set of enterprise context data in the workload input data and the service usage context data; and

creating a consumption context pattern on the basis of the estimated workload resource cost and budget impact relative to the enterprise context data.

13. The method of claim 12, further comprising reporting, by the cloud brokerage computing device, cost consumption data through a graphical user interface on a display device.

14. The method of claim 13, further comprising displaying, by the cloud brokerage computing device, an alert notification through the graphical user interface when a predetermined quota allocation threshold is exceeded, wherein the predetermined quota allocation threshold is determined from the enterprise context data.

15. The method of claim 1, wherein the workload pattern parameter is at least one of a usage pattern associated with the at least one cloud platform, a demand pattern associated with the at least one cloud platform or a quality of service parameter associated with the at least one cloud platform.

32. The non-transitory computer readable medium of claim 1, wherein the non-functional context data comprises at least one of enterprise context data, user persona context data, business workload context data, technical context data, application lifecycle context data, service provider context data, or service usage context data.

33. The non-transitory computer readable medium of claim 32, wherein the enterprise context data comprises at least one of information security rules, governance rules, budget data, business goal targets data, capital and operation expenditure data or business performance data.

34. The non-transitory computer readable medium of claim 32, wherein the user persona context data comprises at least one of j ob profile data, user behavior data, user service usage data, organizational hierarchy data, a social connections index or historical usage data.

35. The non-transitory computer readable medium of claim 32, wherein the technical context data comprises workload resource requirements data, technical interoperability data, location data or data usage data.

36. The non-transitory computer readable medium of claim 32, wherein the service usage context data comprises computing resource consumption data, service usage cost data, or service usage compliance data.

37. The non-transitory computer readable medium of claim 1, wherein the determining compliance with one or more audit and regulatory metrics further comprises:

analyzing the non-functional context data associated with the workload for deviations from one or more retrieved policies comprising a predefined industry regulatory compliance policy, a predefined enterprise regulatory compliance policy, or a predefined governmental regulatory compliance policy; and

triggering an alert notification, based on the analysis, wherein the alert notification is a communicated to a user through a graphical user interface on a display device.

38. The non-transitory computer readable medium of claim 37, wherein the instructions, when executed by the at least one processor, further causes the processor to perform steps further comprising providing a remedial recommendation to a user through the display device, wherein the remedial recommendation comprises information related to enforcing policy compliance of the workload.

39. The non-transitory computer readable medium of claim 37, wherein the instructions, when executed by the at least one processor, further causes the processor to perform steps further comprising:

retrieving at least one first user profile context data from the context repository database;

creating a functional collaboration context on the basis of the at least one first user profile context data, wherein the functional collaboration context associated with the workload is determined based on an organizational hierarchy associated with the at least one first user profile, historical behavior associated with the at least one first user profile, or a social connection context associated with the at least one first user profile; and

communicating a collaboration notification to a second computing device associated with at least one second user, wherein the at least one second computing device is network connected to the at least one first user computing device and wherein;

the network address of the at least one second computing device is determined based on historical behavior data associated with at least one second user profile, the at least one second user profile context data being stored on the context repository database; and

the at least one second user profile is determined on the basis of the functional collaboration context associated with the first user profile.

40. The non-transitory computer readable medium of claim 39, wherein the instructions, when executed by the at least one processor, further causes the processor to perform steps further comprising communicating a deployment approval notification to the second computing device.

41. The non-transitory computer readable medium of claim 40, wherein the instructions, when executed by the at least one processor, further causes the processor to perform steps further comprising deploying the workload only when the deployment approval notification is relayed back by the second computing device.

42. The non-transitory computer readable medium of claim 32, wherein the monitoring cost consumption further comprises:

metering cloud service consumption or computing resource consumption by the workload;

calculating charges associated with the workload on the basis of service subscription data retrieved from a service subscriptions repository database;

validating charges associated with the cloud service consumption or computing resource consumption by the workload against budget data in a set of enterprise context data in the workload input data and the service usage context data; and

creating a consumption context pattern on the basis of the estimated workload resource cost and budget impact relative to the enterprise context data.

43. The non-transitory computer readable medium of claim 42, wherein the instructions, when executed by the at least one processor, further causes the processor to perform steps further comprising reporting cost consumption data through a graphical user interface on a display device.

44. The non-transitory computer readable medium of claim 43, wherein the instructions, when executed by the at least one processor, further causes the processor to perform steps further comprising displaying an alert notification through the graphical user interface when a predetermined quota allocation threshold is exceeded, wherein the predetermined quota allocation threshold is determined from the enterprise context data.

45. The non-transitory computer readable medium of claim 32, wherein the workload pattern parameter is at least one of a usage pattern associated with the at least one cloud platform, a demand pattern associated with the at least one cloud platform or a quality of service parameter associated with the at least one cloud platform.

16. A cloud brokerage device, comprising:

at least one processor and an associated processor readable memory comprising instructions that, when executed by the at least one processor, cause the processor to;

receive workload input data for a workload, wherein the workload input data comprises at least one workload pattern parameter, policy parameter, or cost attribute;

determine resource optimization data based on the workload input data, wherein the determining the resource optimization data comprises determining, compliance of the workload with one or more audit and regulatory metrics, cost consumption of the workload, and non-functional context data associated with the workload, wherein the audit and regulatory metrics include at least one of an enterprise regulatory compliance metric, an industry standards body compliance metric or a governmental regulatory compliance metric and the cost consumption includes metering cost associated with deploying the workload based on a budget;

apply one or more rules to the workload input data, wherein the one or more rules comprise at least one of an information technology, business, compliance, departmental, portfolio or enterprise context rule in a rules repository database; and

deploy the workload across a cloud ecosystem according to deployment plan data generated based on the resource optimization data and the application of the one or more rules to the workload input data, wherein the deployment plan data comprises a deployment plan for optimally deploying the workload that is generated prior to deployment of the workload on any platform.

17. The device of claim 16, wherein the non-functional context data comprises at least one of enterprise context data, user persona context data, business workload context data, technical context data, application lifecycle context data, service provider context data, or service usage context data.

18. The device of claim 17, wherein the enterprise context data comprises at least one of information security rules, governance rules, budget data, business goal targets data, capital and operation expenditure data, or business performance data.

19. The device of claim 17, wherein the user persona context data comprises at least one of job profile data, user behavior data, user service usage data, organizational hierarchy data, a social connections index, or historical usage data.

20. The device of claim 17, wherein the technical context data comprises workload resource requirements data, technical interoperability data, location data, or data usage data.

21. The device of claim 17, wherein the service usage context data comprises computing resource consumption data, service usage cost data, or service usage compliance data.

22. The device of claim 16, wherein the processor readable memory further comprises one or more additional instructions that, when executed by the processor, further cause the processor to:

analyze the non-functional context data associated with the workload for deviations from one or more retrieved policies comprising a predefined industry regulatory compliance policy, a predefined enterprise regulatory compliance policy, or a predefined governmental regulatory compliance policy; and

trigger an alert notification based on the analysis, wherein the alert notification is a communicated to a user through a graphical user interface on a display device triggering an alert notification, wherein the alert notification is a communicated to a user through a graphical user interface on a display device.

23. The device of claim 22, wherein the processor readable memory further comprises one or more additional instructions that, when executed by the processor, further cause the processor to provide a remedial recommendation, wherein the remedial recommendation comprises information related to enforcing policy compliance by the workload.

24. The device of claim 22, wherein the processor readable memory further comprises one or more additional instructions that, when executed by the processor, further cause the processor to:

retrieve a first user profile context data in the captured non-functional context data;

create a functional collaboration context on the basis of the at least one first user profile context data, wherein the functional collaboration context associated with the workload is determined based on an organizational hierarchy associated with the at least one first user profile, historical behavior associated with the at least one first user profile, or a social connection context associated with the at least one first user profile; and

communicate a collaboration notification to a second computing device associated with a second user, wherein the second computing device is network connected to the first user computing device and wherein;

the network address of the second computing device is determined based on historical behavior data associated with a second user profile, the second user profile context data being stored on the context repository database; and

the second user profile is determined on the basis of the functional collaboration context associated with the first user profile.

25. The device of claim 24, wherein the processor readable memory further comprises one or more additional instructions that, when executed by the processor, further cause the processor to communicate a deployment approval notification to the second computing device.

26. The device of claim 25, wherein the processor readable memory further comprises one or more additional instructions that, when executed by the processor, further cause the processor to deploy the workload only when the deployment approval notification is relayed back by the second computing device.

27. The device of claim 16, wherein the processor readable memory further comprises one or more additional instructions that, when executed by the processor, further cause the processor to:

meter computing resource consumption or cloud service consumption of the workload;

compute charges associated with the workload on the basis of service subscription data retrieved from a service subscriptions repository database;

validate charges associated with actual computing resource or cloud service consumption by the workload against budget data in a set of enterprise context data in the workload input data and the service usage context data; and

create a consumption context pattern on the basis of the estimated workload resource cost and budget impact relative to the enterprise context data.

28. The device of claim 27, wherein the processor readable memory further comprises one or more additional instructions that, when executed by the processor, further cause the processor to report cost consumption data through a graphical user interface on a display device.

29. The device of claim 28, wherein the processor readable memory further comprises one or more additional instructions that, when executed by the processor, further cause the processor to display an alert notification through the graphical user interface when a predetermined quota allocation threshold is exceeded, wherein the predetermined quota allocation threshold is determined from the enterprise context data.

30. The device of claim 16, wherein the workload pattern parameter is at least one of a usage pattern associated with the at least one cloud platform, a demand pattern associated with the at least one cloud platform or a quality of service parameter associated with the at least one cloud platform.

31. A non-transitory computer readable medium having stored thereon instructions for improved deployment of workloads across cloud platforms of an integrated cloud ecosystem, which, when executed by at least one processor, causes the processor to perform steps comprising:

receiving workload input data for a workload, wherein the workload input data comprises at least one workload pattern parameter, policy parameter, or cost attribute;

determining resource optimization data based on the workload input data, wherein the determining the resource optimization data comprises determining, compliance of the workload with one or more audit and regulatory metrics, cost consumption of the workload, and non-functional context data associated with the workload, wherein the audit and regulatory metrics include at least one of an enterprise regulatory compliance metric, an industry standards body compliance metric or a governmental regulatory compliance metric and the cost consumption includes metering cost associated with deploying the workload based on a budget;

applying one or more rules to the workload input data, wherein the one or more rules comprise at least one of an information technology, business, compliance, departmental, portfolio or enterprise context rule in a rules repository database; and

deploying the workload across a cloud ecosystem according to deployment plan data generated based on the resource optimization data and the application of the one or more rules to the workload input data, wherein the deployment plan data comprises a deployment plan for optimally deploying the workload that is generated prior to deployment of the workload on any platform.