Automated analysis and dynamic resource configuration for hard provisioned virtual machines

Patent metadata

Field	Value
Publication number	US-12182603-B2
Application number	US-202217725350-A
Country	US
Kind code	B2
Filing date	Apr 20, 2022
Priority date	Apr 20, 2022
Publication date	Dec 31, 2024
Grant date	Dec 31, 2024

Abstract

Methods and systems for managing provisioning of virtual machines. Virtual machines may host applications that may provide computer implemented services. Various hardware resources may be allocated to the virtual machines via a hypervisor. As the workloads of the applications change, the virtual machines may become over or under provisioned. To manage provisioning of virtual machines, various types of resource consumption estimates may be obtained. The resource consumption estimates may be used to ascertain how to provision various virtual machines to reduce or eliminate inefficient allocations of hardware resources for use by the virtual machines.

First claim

What is claimed is:

1. A computer-implemented method for managing resources allocated to virtual machines, the method comprising:
obtaining a first resource consumption estimate for a hard provisioned virtual machine of the virtual machines, the first resource consumption estimate being obtained from a hypervisor that manages resource provisioning for the hard provisioned virtual machine;
obtaining a second resource consumption estimate for a hard provisioned virtual machine of the virtual machines, the second resource consumption estimate being obtained from an agent hosted by the hard provisioned virtual machine;
identifying:
a resource inefficiency for the hard provisioned virtual machine based on the first resource consumption estimate and the second resource consumption estimate, and
a new resource allocation based on the resource inefficiency; and

remediating the identified resource inefficiency using the new resource allocation.

2. The computer-implemented method of claim 1 , wherein obtaining the second resource consumption estimate comprises:
obtaining workload characteristics of workloads performed by the hard provisioned virtual machines;
classifying the workloads based on the workload characteristics;
calculating a reduction factor based on the workload classifications; and
reducing a quantity of computing resources alleged to be used by the hard provisioned virtual machine by the reduction factor to obtain the second resource consumption estimate.

3. The computer-implemented method of claim 2 , wherein the workloads are classified based on a productivity level of the workloads.

4. The computer-implemented method of claim 3 , wherein the productivity level of each of the workloads is based on a ratio of polling behavior by a respective workload and non-polling behavior by the respective workload.

5. The computer-implemented method of claim 4 , wherein the polling behavior corresponds to acts of the workload inquiring regarding whether actions should be performed by the hard provisioned virtual machine.

6. The computer-implemented method of claim 1 , wherein identifying the resource inefficiency comprises:
making a determination that the first resource consumption estimate and the second resource consumption estimate are different;
based on the determination:
identifying a first provisional resource inefficiency based on the first resource consumption estimate, and
identifying a second provisional resource inefficiency based on the second resource consumption estimate; and

obtaining the resource inefficiency using the first provisional resource inefficiency and the second provisional resource inefficiency.

7. The computer-implemented method of claim 1 , wherein remediating the identified resource inefficiency comprises:
making a determination that the first resource consumption estimate and the second resource consumption estimate are different; and
based on the determination:
requesting authorization to modify a resource allocation for the hard provisioned virtual machine based on the new resource allocation.

8. The computer-implemented method of claim 1 , wherein remediating the identified resource inefficiency comprises:
making a determination that the first resource consumption estimate and the second resource consumption estimate are similar; and
based on the determination:
provisioning the hard provisioned virtual machine based on the new resource allocation.

9. The computer-implemented method of claim 1 , wherein the virtual machines are hosted by a data processing system, and the virtual machines comprise a soft provisioned virtual machine which utilizes, at least in part, shared resources of the data processing system, and the hard provisioned virtual machine has exclusive use of a portion of resources of the data processing system.

10. The computer-implemented method of claim 1 , wherein the first resource consumption estimate is based on resources consumed by the hard provisioned virtual machine for any action, and the second resource consumption estimate is based on resources consumed by the hard provisioned virtual machine for only productive actions.

11. A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for managing resources allocated to virtual machines, the operations comprising:
obtaining a first resource consumption estimate for a hard provisioned virtual machine of the virtual machines, the first resource consumption estimate being obtained from a hypervisor that manages resource provisioning for the hard provisioned virtual machine;
obtaining a second resource consumption estimate for a hard provisioned virtual machine of the virtual machines, the second resource consumption estimate being obtained from an agent hosted by the hard provisioned virtual machine;
identifying:
a resource inefficiency for the hard provisioned virtual machine based on the first resource consumption estimate and the second resource consumption estimate, and
a new resource allocation based on the resource inefficiency; and

remediating the identified resource inefficiency using the new resource allocation.

12. The non-transitory machine-readable medium of claim 11 , wherein obtaining the second resource consumption estimate comprises:
obtaining workload characteristics of workloads performed by the hard provisioned virtual machines;
classifying the workloads based on the workload characteristics;
calculating a reduction factor based on the workload classifications; and
reducing a quantity of computing resources alleged to be used by the hard provisioned virtual machine by the reduction factor to obtain the second resource consumption estimate.

13. The non-transitory machine-readable medium of claim 12 , wherein workloads are classified based on a productivity level of the workloads.

14. The non-transitory machine-readable medium of claim 13 , wherein the productivity level of each of the workloads is based on a ratio of polling behavior by a respective workload and non-polling behavior by the respective workload.

15. The non-transitory machine-readable medium of claim 14 , wherein the polling behavior corresponds to acts of the workload inquiring regarding whether actions should be performed by the hard provisioned virtual machine.

16. A data processing system, comprising:
a processor; and
a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for managing resources allocated to virtual machines, the operations comprising:
obtaining a first resource consumption estimate for a hard provisioned virtual machine of the virtual machines, the first resource consumption estimate being obtained from a hypervisor that manages resource provisioning for the hard provisioned virtual machine;
obtaining a second resource consumption estimate for a hard provisioned virtual machine of the virtual machines, the second resource consumption estimate being obtained from an agent hosted by the hard provisioned virtual machine;
identifying:
a resource inefficiency for the hard provisioned virtual machine based on the first resource consumption estimate and the second resource consumption estimate, and
a new resource allocation based on the resource inefficiency; and

remediating the identified resource inefficiency using the new resource allocation.

17. The data processing system of claim 16 , wherein obtaining the second resource consumption estimat

Assignees

• Dell Products Lp

Inventors

• Caisse Joseph B
• Goodman Bradley K
• Tummala Himabindu

Classifications

• G06F9/5077

  Logical partitioning of resources; Management or configuration of virtualized resources (specific details on emulation or internal functioning of virtual machines G06F9/455) · CPC title

• G06F2209/5019

  Workload prediction · CPC title

• G06F9/5005

  to service a request · CPC title

• G06F2009/45583

  Memory management, e.g. access or allocation · CPC title

• G06F2009/45579

  I/O management, e.g. providing access to device drivers or storage · CPC title