SYSTEMS AND METHODS FOR PROVIDING DECISION TIME BROKERAGE IN A HYBRID CLOUD ECOSYSTEM

• US 20130198050A1
• Filed: 01/31/2013
• Published: 08/01/2013
• 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
• Granted Patent: US 9,967,159 B2
• Time in Patent Office: Days
• US Class Current: 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 providing decision-time brokerage in an integrated cloud ecosystem, the method comprising:

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, wherein the computing device comprises at least one processor and an associated processor readable memory, and wherein;

the workload input is at least one input selected from a group consisting of at least one workload pattern parameter associated with at least one cloud platform of a cloud service provider, at least one policy parameter associated with the cloud platform, and at least one cost attribute associated with the cloud platform;

determining resource optimization for the workload input by the brokerage engine, wherein determining comprises;

monitoring the workload input for compliance with one or more audit and regulatory metrics, wherein the audit and regulatory metrics include at least one of an enterprise regulatory compliance metric, an industry standards body compliance metric and a governmental regulatory compliance metric;

monitoring the cost consumption of the workload input, wherein cost consumption includes metering cost associated with deploying the workload on a cloud platform;

capturing non-functional context data associated with the workload input into a context repository database; and

applying one or more rules to the workload, wherein a rule is at least one of an information technology, business, compliance, departmental, portfolio or enterprise context rule in a rules repository database communicably coupled with the brokerage engine and applying comprises optimizing provisioning of the workload across one or more cloud platform on the basis of the one or more rules; and

deploying, by the brokerage engine, the workload across the one or more cloud platforms.

2. The method of claim 1, wherein 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, and service usage context data.

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

4. The method of claim 2, wherein 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 and historical usage data.

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

6. The method of claim 2, wherein application lifecycle context data comprises at least one of application lifecycle stage data, project schedule data, project service level agreements (SLA) data, project requirements data, project budgeting data, project portfolio and environment data.

7. The method of claim 2, wherein service provider context data comprises at least of a service provider offerings catalogue, policy compliance history, notifications data, service non-functional historical context data, future service offerings roadmap data, service consumption data, service provider financial health data and service interoperability data.

8. The method of claim 2, wherein service usage context data comprises computing resource consumption data, service usage cost data, and service usage compliance data.

9. The method of claim 1, wherein monitoring the workload input for compliance with one or more audit and regulatory metrics comprises:

retrieving one or more of a predefined industry regulatory compliance policy, a predefined enterprise regulatory compliance policy and a predefined governmental regulatory compliance policy from an audit datastore repository communicably connected to the brokerage engine;

analyzing the non-functional context information in the workload input for deviations from the one or more retrieved policies; and

triggering an alert notification, wherein the alert notification is a communicated to a user through a graphical user interface on a display means communicably connected to the computing device.

10. The method of claim 9, further comprising providing a remedial recommendation to a user through the display means, wherein the remedial recommendation provides information related to enforcing policy compliance of the workload input.

11. The method of claim 9, further comprising:

capturing one or more events by the brokerage engine, wherein the event is identified thereafter if the event is at least one event selected from information technology or network events, identity and access related events, asset configuration management related events, and operational security events; and

performing an analysis of the impact of the one or more events on the business value of an enterprise.

12. The method of claim 9, further comprising triggering a social workflow by the brokerage engine, wherein a social workflow includes:

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 input is determined at least by;

determining an organizational hierarchy associated with the at least one first user profile;

determining historical behavior associated with the at least one first user profile; and

determining a social connection context associated with the at least one first user profile; and

communicating a collaboration notification to a computing device associated with at least one second user, wherein the at least one second user 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 user computing device is determined by means of historical behavior data associated with the 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.

13. The method of claim 12, further comprising communicating a deployment approval notification to the second user computing device.

14. The method of claim 13, further comprising deploying the workload only if the deployment approval notification is relayed back to the brokerage engine by the second user computing device.

15. The method of claim 1, wherein monitoring cost consumption of the workload input comprises:

receiving service provider subscription data from a service subscriptions repository database communicably connected to the brokerage engine; and

reporting cost data to an end user by means of an output display device.

16. The method of claim 15, wherein monitoring cost consumption further comprises:

metering cloud service consumption by the workload;

calculating charges associated with the workload on the basis of service subscription data retrieved from a service subscriptions repository database communicably coupled to the brokerage engine;

validating charges associated with the cloud service consumption by the workload against budget data in a set of enterprise context data in the workload 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.

17. The method of claim 16, further comprising reporting cost consumption data through a graphical user interface on a display means communicably connected to the computing device.

18. The method of claim 16, further comprising refining the consumption context pattern iteratively in successive cost consumption monitoring activity associated with a specific user persona context data set in an input workload.

19. The method of claim 17, further comprising displaying an alert notification through the graphical user interface if a predetermined quota allocation threshold is exceeded, wherein the predetermined quota allocation threshold is determined from the enterprise context data.

20. The method of claim 16, further comprising triggering a social workflow by the brokerage engine, wherein a social workflow includes:

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

creating a functional collaboration context on the basis of the first user profile context data, wherein the functional collaboration context associated with the workload input is determined at least by;

determining an organizational hierarchy associated with the first user profile;

determining historical behavior associated with the first user profile; and

determining a social connection context associated with the first user profile; and

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

the network address of the second user computing device is determined by means of historical behavior data associated with the 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.

21. The method of claim 20, further comprising communicating a deployment approval notification to the second user computing device.

22. The method of claim 21, further comprising deploying the workload only if the deployment approval notification is relayed back to the brokerage engine by the second user computing device.

23. 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 and a quality of service parameter associated with the at least one cloud platform.

24. The method of claim 1, wherein the technology architecture context identified in the workload input is validated against an enterprise architecture, the enterprise architecture extracted from an enterprise architecture repository communicably connected to the brokerage engine.

25. A cloud brokerage system, the cloud brokerage system comprising:

a brokerage engine executed in a computing device communicably connected to at least one cloud platform of a cloud service provider, wherein the computing device comprises at least one processor and an associated processor readable memory and the cloud platform is a networked computing environment, and wherein the brokerage engine is configured to;

receive 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, wherein the computing device comprises at least one processor and an associated processor readable memory, and wherein;

the workload input is at least one input selected from a group consisting of at least one workload pattern parameter associated with at least one cloud platform of a cloud service provider, at least one policy parameter associated with the cloud platform, and at least one cost attribute associated with the cloud platform;

determine resource optimization for the workload input by the brokerage engine, wherein determining comprises;

monitor the workload input for compliance with one or more audit and regulatory metrics, wherein the audit and regulatory metrics include at least one of an enterprise regulatory compliance metric, an industry standards body compliance metric and a governmental regulatory compliance metric;

monitor the cost consumption of the workload input, wherein cost consumption includes metering cost associated with deploying the workload on a cloud platform;

capture non-functional context data associated with the workload input into a context repository database; and

apply one or more rules to the workload, wherein a rule is at least one of an information technology, business, compliance, departmental, portfolio or enterprise context rule in a rules repository database communicably coupled with the brokerage engine and applying comprises optimizing provisioning of the workload across one or more cloud platform on the basis of the one or more rules; and

recommend a deployment plan for the workload across the one or more cloud platforms to user through a communicably coupled display means.

26. The system of claim 25, wherein 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, and service usage context data.

27. The system of claim 26, wherein enterprise context data comprises at least one of information security rules, governance rules, budget data, business goal targets data, capital and operation expenditure data, and business performance data.

28. The system of claim 26, wherein 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, and historical usage data.

29. The system of claim 26, wherein technical context data comprises workload resource requirements data, technical interoperability data, location data, and data usage data.

30. The system of claim 26, wherein service usage context data comprises computing resource consumption data, service usage cost data, and service usage compliance data.

31. The system of claim 25, wherein monitoring the workload input for compliance with one or more audit and regulatory metrics comprises:

retrieving one or more of a predefined industry regulatory compliance policy, a predefined enterprise regulatory compliance policy and a predefined governmental regulatory compliance policy from an audit datastore repository communicably connected to the brokerage engine;

analyzing the non-functional context information in the workload input for deviations from the one or more retrieved policies; and

triggering an alert notification, wherein the alert notification is a communicated to a user through a graphical user interface on a display means communicably connected to the computing device.

32. The system of claim 31, further comprising providing a remedial recommendation to a user through the display means, wherein the remedial recommendation provides information related to enforcing policy compliance by the workload input.

33. The system of claim 31, further comprising triggering a social workflow by the brokerage engine, wherein a social workflow includes:

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

creating a functional collaboration context on the basis of the first user profile context data, wherein the functional collaboration context associated with the workload input is determined at least by;

determining an organizational hierarchy associated with the first user profile;

determining historical behavior associated with the first user profile; and

determining a social connection context associated with the first user profile; and

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

the network address of the second user computing device is determined by means of historical behavior data associated with the 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.

34. The system of claim 33, further comprising communicating a deployment approval notification to the second user computing device.

35. The system of claim 34, further comprising deploying the workload only if the deployment approval notification is relayed back to the brokerage engine by the second user computing device.

36. The system of claim 25, wherein monitoring cost consumption of the workload input comprises:

receiving service provider subscription data from a service subscriptions repository database communicably connected to the brokerage engine; and

reporting cost data to an end user by means of an output display device.

37. The system of claim 36, wherein monitoring cost consumption further comprises:

metering computing resource consumption of the workload input;

computing charges associated with the workload on the basis of service subscription data retrieved from a service subscriptions repository database communicably coupled to the brokerage engine;

validating charges associated with actual computing resource consumption by the workload against budget data in a set of enterprise context data in the workload 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.

38. The system of claim 37, further comprising refining the consumption context pattern iteratively in successive cost consumption monitoring activity associated with a specific user persona context data set in an input workload.

39. The system of claim 37, further comprising reporting cost consumption data through a graphical user interface on a display means communicably connected to the computing device.

40. The system of claim 39, further comprising displaying an alert notification through the graphical user interface if a predetermined quota allocation threshold is exceeded, wherein the predetermined quota allocation threshold is determined from the enterprise context data.

41. The system of claim 37, further comprising triggering a social workflow by the brokerage engine, wherein a social workflow includes:

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

creating a functional collaboration context on the basis of the first user profile context data, wherein the functional collaboration context associated with the workload input is determined at least by;

determining an organizational hierarchy associated with the first user profile;

determining historical behavior associated with the first user profile; and

determining a social connection context associated with the first user profile; and

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

the network address of the second user computing device is determined by means of historical behavior data associated with the 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.

42. The system of claim 41, further comprising communicating a deployment approval notification to the second user computing device.

43. The system of claim 42, further comprising deploying the workload only if the deployment approval notification is relayed back to the brokerage engine by the second user computing device.

44. The system of claim 25, 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 and a quality of service parameter associated with the at least one cloud platform.

45. A non-transitory computer readable medium having stored thereon instructions for performing a method for providing decision-time brokerage in an integrated cloud ecosystem, which, when executed by at least one processor, causes the processor to perform steps comprising:

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, wherein the computing device comprises at least one processor and an associated processor readable memory, and wherein;

the workload input is at least one input selected from a group consisting of at least one workload pattern parameter associated with at least one cloud platform of a cloud service provider, at least one policy parameter associated with the cloud platform, and at least one cost attribute associated with the cloud platform;

determining run-time resource optimization for the workload input by the brokerage engine, wherein determining comprises;

monitoring the workload input for compliance with one or more audit and regulatory metrics, wherein the audit and regulatory metrics include at least one of an enterprise regulatory compliance metric and a governmental regulatory compliance metric;

monitoring the cost consumption of the workload input, wherein cost consumption includes metering cost associated with deploying the workload on a cloud platform, and monitoring comprises;

metering computing resource consumption of the workload input;

computing charges associated with the workload on the basis of service subscription data retrieved from a service subscriptions repository database communicably coupled to the brokerage engine;

validating charges associated with actual computing resource consumption by the workload against budget data in a set of enterprise context data in the workload 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; and

capturing non-functional context data associated with the workload input into a context repository database; and

applying one or more rules to the workload, wherein a rule is at least one of an infrastructural, business, compliance, departmental, portfolio or enterprise context rule in a rules repository database communicably coupled with the brokerage engine and applying comprises optimizing provisioning of the workload across one or more cloud platform on the basis of the one or more rules; and

deploying, by the brokerage engine, the workload across the one or more cloud platforms.