TECHNIQUES FOR INTELLIGENT SERVICE DEPLOYMENT

• US 20140344461A1
• Filed: 07/31/2014
• Published: 11/20/2014
• Est. Priority Date: 03/19/2010
• Status: Abandoned Application

• Current Assignee: Micro Focus Software, Inc. (Open Text Corporation)
• Original Assignee: Novell Incorporated (Open Text Corporation)
• Inventors: Carter, Stephen R., Sabin, Jason Allen, Jorgensen, Michael John, Kranendonk, Nathaniel Brent, Larsen, Kal A.
• Application Number:
  US14/448,468
• Publication Number:
  US 20140344461A1
• Time in Patent Office: Days
• US Class Current: 709/226
• CPC Class Codes: G06F 8/60 Software deployment
• CPC Class Codes: G06F 9/5072 Grid computing
• CPC Class Codes: H04L 41/0806 for initial configuration or provisioning, e.g. plug-and-play
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/60 Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources admission control or resource allocation H04L47/70

Abstract

Techniques for intelligent service deployment are provided. Cloud and service data are evaluated to develop a service deployment plan for deploying a service to a target cloud processing environment. When dictated by the plan or by events that trigger deployment, the service is deployed to the target cloud processing environment in accordance with the service deployment plan.

Claims

1. (canceled)

2. method, comprising:

obtaining attribute data for a cloud and a service;

developing a service placement plan for deploying the service based on a weighted evaluation of the attribute data; and

deploying the service to the cloud in accordance with the developed service placement plan.

3. The method of claim 2, wherein obtaining further includes identifying a collection of workloads within the service.

4. The method of claim 2, wherein obtaining further include identifying at least a portion of the attribute data as a cloud reputation for the cloud

5. The method of claim 4, wherein identifying further includes identifying another portion of the attribute data as a cloud geography for the cloud.

6. The method of claim 5, wherein identifying further includes identifying still another portion of the attribute data as one or more of:

a cloud service-level agreement, cloud expense data, and a cloud state.

7. The method of claim 2, wherein developing further includes defining a sequence for deployment of workloads and software products that comprise the server within the service placement plan.

8. The method of claim 2, wherein developing further includes providing data relevant to the service for configuration of the service, a service-level agreement for the service, a service reputation for the service, and service expense data within the service placement plan.

9. The method of claim 2, wherein deploying further includes processing directives of the service placement plan to deploy the service to a target cloud.

10. The method of claim 2, wherein deploying further includes receiving metrics back from the service when the service is operational within the target cloud.

11. A method, comprising:

weighting attribute data associated with a service placement plan for a service that is to be deployed to a target cloud; and

processing directives of the service placement plan based on the weighted attribute data to deploy the service to the target cloud.

12. The method of claim 11, wherein weighting further includes weighting component attributes of the attribute data, wherein each component attribute is one of:

a service-level agreement, a target cloud reputation, a target cloud geography, a target cloud state, and a target cloud expense data.

13. The method of claim 11, wherein weighting further includes altering a mix of workloads and software products that comprise the service based on the weighted attribute data.

14. The method of claim 11, wherein weighting further includes identifying the target cloud based on the weighted attribute data.

15. The method of claim 11, wherein processing further includes receiving dynamic alters relevant to events and usage metrics and in response thereto dynamically re-developing the service placement plan.

16. The method of claim 11 further comprising, receiving metrics for the service and the target cloud once the service is deployed and operational in the target cloud

17. The method of claim 16 further comprising, auditing the service placement plan in response to the received metrics.

18. A system, comprising:

a processor; and

a service deployment planner configured and adapted to;

i) execute on the processor, ii) develop a plan to deploy a service to a target cloud based on an evaluation of weighted attribute data associated with the service and a cloud network having the target cloud, and iii) dynamically revise the plan in response to real-time alerts.

19. The system of claim 18, wherein the system further includes a deployment manager configured and adapted to:

i) execute on the processor, ii) follow directives of the plan to configure the service for deployment to the target cloud, and iii) deploy the service to the target cloud.

20. The system of claim 18, wherein the service deployment planner is further configured and adapted to audit the plan in response to metrics returned from the target cloud with the service deployed therein.

21. system of claim 18, wherein the weighted attribute data includes one or more of:

target cloud expense data, a target cloud reputation rating, a service-level agreement for the service with the target cloud, and a geography for a location of the target cloud.