CLOUD INDEPENDENT TUNING SERVICE FOR AUTONOMOUSLY MANAGED WORKLOADS

• US 20160099888A1
• Filed: 12/10/2014
• Published: 04/07/2016
• Est. Priority Date: 10/03/2014
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: DUNBAR, Anthony M., FALL, James J., HANSON, Andrew L., MATZEK, Samuel D., ROMANO, George J., VAN HOVE, Tammy L.
• Granted Patent: US 9,998,399 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: H04L 47/76 using dynamic resource allocation, e.g. in-call renegotiation requested by the user or requested by the network in response to changing network conditions
• CPC Class Codes: H04L 47/827 Aggregation of resource allocation or reservation requests
• CPC Class Codes: H04L 67/1008 based on parameters of servers, e.g. available memory or workload monitoring of computer activity G06F11/30

Abstract

Method to perform an operation comprising receiving, from a workload in a cloud computing environment, an abstract request to modify resources allocated to the workload, translating, by a tuning service, the abstract request to a set of requirements to fulfill the abstract request, identifying, by the tuning service, a resource configuration suitable to satisfy the set of requirements, and providing, by the tuning service, the resource configuration to fulfill the abstract request.

Claims

1. A method, comprising:

receiving, from a workload in a cloud computing environment, an abstract request to modify resources allocated to the workload;

translating, by a tuning service, the abstract request to a set of requirements to fulfill the abstract request;

identifying, by the tuning service, a resource configuration suitable to satisfy the set of requirements; and

providing, by the tuning service, the resource configuration to fulfill the abstract request.

2. The method of claim 1, wherein the resource configuration comprises a modified computing resource allocation, wherein the modified computing resource allocation modifies at least one of:

(i) a hardware resource, (ii) a quality of service, and (iii) a software resource provided to the workload.

3. The method of claim 1, wherein the resource configuration comprises moving the workload to a different cloud computing environment.

4. The method of claim 1, wherein the resource configuration comprises cloning the workload.

5. The method of claim 1, wherein the abstract request comprises a generic request that is agnostic relative to the cloud computing environment.

6. The method of claim 1, wherein a resource utilization of the workload is monitored by at least one of:

(i) the workload, and (ii) the tuning service.

7. The method of claim 1, wherein the cloud computing environment provides a baseline level of resources to the workload upon deployment, wherein the workload and the tuning service are configured to modify the baseline level of resources independent of the configuration of the cloud computing environment.