Dynamically move heterogeneous cloud resources based on workload analysis

• US 9,246,840 B2
• Filed: 12/13/2013
• Issued: 01/26/2016
• Est. Priority Date: 12/13/2013
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Anderson, Jason L., Bhatia, Nimesh, Boss, Gregory J., Singh, Animesh
• Primary Examiner(s): Srivastava, Vivek
• Assistant Examiner(s): Barker, Todd L
• Application Number:
  US14/106,547
• Publication Number:
  US 20150172205A1
• Time in Patent Office: 774 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 9/5077 Logical partitioning of resources; Management or configuration of virtualized resources specific details on emulation or internal functioning of virtual machines G06F9/455
• CPC Class Codes: H04L 47/70 Admission control; Resource allocation

Abstract

An approach is provided for an information handling system to scale a workload being executed in a cloud computing environment. In the approach, performance of the workload is analyzed by using more than one resource configuration. The resource configurations use different resources that are available in the cloud computing environment. The analysis of the workload results in test results. At least one of the resource configurations is directed to a scaling up of resources that alters resources assigned to a first virtual machine (VM) running the workload. Another resource configuration is directed to a scaling out of resources that adds one or more VMs to a second VM creating a number of VMs running the workload. The test results are compared and an optimal test result is selected. A workload scaling profile is optimized according to the selected optimal test result.

Claims

1. A method, in an information handling system comprising a processor and a memory, of scaling a workload being executed in a cloud computing environment, the method comprising:

providing incoming traffic of the workload concurrently to both a scaled up resource configuration and a scaled out resource configuration, wherein the scaled up resource configuration adds additional resources to a first virtual machine (VM) running the workload, and wherein the scaled out resource configuration adds one or more VMs to a second VM to create a first plurality of VMs running the workload;

comparing a plurality of test results generated from the scaled up resource configuration and the scaled out resource configuration, the comparison resulting in an optimal resource configuration; and

optimizing a workload scaling profile based upon the optimized resource configuration.

2. The method of claim 1 further comprising:

creating a mirror image of the first VM to create the second VM, wherein the first and second VMs are both running in the cloud computing environment; and

processing the incoming traffic by both the first VM and the first plurality of VMs to create a first set of test results corresponding to the scaled up resource configuration and a second set of test results corresponding to the scaled out resource configuration, wherein both the first and second sets of test results are included in the plurality of test results.

3. The method of claim 2 wherein at least one of the additional resources added to the first VM is selected from the group consisting of additional CPU resources, additional memory, additional IP addresses, and additional network bandwidth.

4. The method of claim 1 further comprising:

prior to the inputting of the incoming traffic to both the scaled up resource configuration and the scaled out resource configuration, detecting that the workload is stressed by comparing one or more performance metrics to one or more thresholds; and

performing the inputting, the comparing, and the optimizing in response to the workload not having the workload scaling profile prior to the detecting.

5. The method of claim 1 further comprising:

prior to the inputting of the incoming traffic to both the scaled up resource configuration and the scaled out resource configuration, detecting that the workload is stressed by comparing one or more performance metrics to one or more thresholds; and

in response to determining that the workload has an existing workload scaling profile, updating the existing workload scaling profile based upon the optimized resource configuration.

6. An information handling system comprising:

one or more processors;

a memory coupled to at least one of the processors; and

a set of instructions stored in the memory and executed by at least one of the processors to scale a workload being executed in a cloud computing environment, wherein the set of instructions perform actions of;

providing incoming traffic of the workload concurrently to both a scaled up resource configuration and a scaled out resource configuration, wherein the scaled up resource configuration adds additional resources to a first virtual machine (VM) running the workload, and wherein the scaled out resource configuration adds one or more VMs to a second VM to create a first plurality of VMs running the workload;

comparing a plurality of test results generated from the scaled up resource configuration and the scaled out resource configuration, the comparison resulting in an optimal resource configuration; and

optimizing a workload scaling profile based upon the optimized resource configuration.

7. The information handling system of claim 6 where the actions further comprise:

creating a mirror image of the first VM to create the second VM, wherein the first and second VMs are both running in the cloud computing environment; and

processing the incoming traffic by both the first VM and the first plurality of VMs to create a first set of test results corresponding to the scaled up resource configuration and a second set of test results corresponding to the scaled out resource configuration, wherein both the first and second sets of test results are included in the plurality of test results.

8. The information handling system of claim 7 wherein at least one of the additional resources added to the first VM is selected from the group consisting of additional CPU resources, additional memory, additional IP addresses, and additional network bandwidth.

9. The information handling system of claim 6 where the actions further comprise:

prior to the inputting of the incoming traffic to both the scaled up resource configuration and the scaled out resource configuration, detecting that the workload is stressed by comparing one or more performance metrics to one or more thresholds; and

performing the inputting, the comparing, and the optimizing in response to the workload not having the workload scaling profile prior to the detecting.

10. The information handling system of claim 6 where the actions further comprise:

prior to the inputting of the incoming traffic to both the scaled up resource configuration and the scaled out resource configuration, detecting that the workload is stressed by comparing one or more performance metrics to one or more thresholds; and

in response to determining that the workload has an existing workload scaling profile, updating the existing workload scaling profile based upon the optimized resource configuration.

11. A computer program product stored in a non-transitory computer readable medium, comprising computer instructions that, when executed by an information handling system, causes the information handling system to scale a workload being executed in a cloud computing environment by performing actions comprising:

providing incoming traffic of the workload concurrently to both a scaled up resource configuration and a scaled out resource configuration, wherein the scaled up resource configuration adds additional resources to a first virtual machine (VM) running the workload, and wherein the scaled out resource configuration adds one or more VMs to a second VM to create a first plurality of VMs running the workload;

comparing a plurality of test results generated from the scaled up resource configuration and the scaled out resource configuration, the comparison resulting in an optimal resource configuration; and

optimizing a workload scaling profile based upon the optimized resource configuration.

12. The computer program product of claim 11 where the actions further comprise:

creating a mirror image of the first VM to create the second VM, wherein the first and second VMs are both running in the cloud computing environment; and

processing the incoming traffic by both the first VM and the first plurality of VMs to create a first set of test results corresponding to the scaled up resource configuration and a second set of test results corresponding to the scaled out resource configuration, wherein both the first and second sets of test results are included in the plurality of test results.

13. The computer program product of claim 12 wherein at least one of the additional resources added to the first VM is selected from the group consisting of additional CPU resources, additional memory, additional IP addresses, and additional network bandwidth.

14. The computer program product of claim 11 where the actions further comprise:

prior to the inputting of the incoming traffic to both the scaled up resource configuration and the scaled out resource configuration, detecting that the workload is stressed by comparing one or more performance metrics to one or more thresholds; and

performing the inputting, the comparing, and the optimizing in response to the workload not having the workload scaling profile prior to the detecting.

15. The computer program product of claim 11 where the actions further comprise:

prior to the inputting of the incoming traffic to both the scaled up resource configuration and the scaled out resource configuration, detecting that the workload is stressed by comparing one or more performance metrics to one or more thresholds; and

in response to determining that the workload has an existing workload scaling profile, updating the existing workload scaling profile based upon the optimized resource configuration.