Utilizing machine learning to reduce cloud instances in a cloud computing environment
A device receives, from a cloud computing environment, cloud instance information associated with cloud instances in the cloud computing environment, and processes the cloud instance information, with a machine learning model, to determine containers for one or more of the cloud instances and whether cloud instances should be removed from the cloud computing environment. The device causes a first subset of the cloud instances to be removed from the cloud computing environment, based on determining which of the cloud instances should be removed, and causes the containers to be created for a second subset of the cloud instances based on determining the containers. The device receives, from the cloud computing environment, cloud container information associated with the containers created in the cloud computing environment, and causes one or more of the containers to be scaled based on the cloud container information.
1. A device, comprising:
one or more memories; and
one or more processors, communicatively coupled to the one or more memories, to:
receive, from a cloud computing environment, cloud instance information associated with a first set of cloud instances in the cloud computing environment;
determine, based on processing the first set of cloud instances with a machine learning model, similarities between cloud instances included in the first set of cloud instances;
create a first container for a first subset of the cloud instances, based on the similarities between the cloud instances included in the first set of cloud instances; and
cause the first container to execute at least one application instance,
the at least one application instance providing one or more services that correspond to services that were executed by the first subset of the cloud instances.
2. The device of claim 1 , wherein the one or more processors are further to:
create a second container for a second subset of the cloud instances, based on the similarities between the cloud instances included in the first set of cloud instances,
the second subset of the cloud instances being different from the first subset of the cloud instances.
3. The device of claim 2 , wherein the one or more processors are further to:
cause the second container to execute another application instance,
the other application instance providing one or more services that correspond to services that were executed by the second subset of the cloud instances.
4. The device of claim 1 , wherein the one or more processors are further to:
identify a second subset of the cloud instances based on the similarities between the cloud instances included in the first set of cloud instances,
the second subset of the cloud instances being different from the first subset of the cloud instances; and
remove the second subset of the cloud instances from the first set of cloud instances, causing the second subset of the cloud instances to cease operation.
5. The device of claim 1 , wherein each application instance, of the at least one application instance, shares an operating system kernel with other application instances of the at least one application instance.
6. The device of claim 1 , wherein the one or more processors are further to:
determine to create the first container based on a measure of utilization associated with similar cloud instances included in the first set of cloud instances.
7. The device of claim 1 , wherein the one or more processors, when creating the first container, are to:
create the first container further based on:
predicted processor utilization output from the machine learning model, and
predicted memory utilization output from the machine learning model.
8. A non-transitory computer-readable medium storing instructions, the instructions comprising:
one or more instructions that, when executed by one or more processors of a device, cause the one or more processors to:
receive, from a cloud computing environment, cloud instance information associated with a first set of cloud instances in the cloud computing environment;
determine, based on processing the first set of cloud instances with a machine learning model, similarities between cloud instances included in the first set of cloud instances;
create a first container for a first subset of the cloud instances, based on the similarities between the cloud instances included in the first set of cloud instances; and
cause the first container to execute at least one application instance,
the at least one application instance providing one or more services that correspond to services that were executed by the first subset of the cloud instances.
9. The non-transitory computer-readable medium of claim 8 , wherein the one or more instructions further cause the one or more processors to:
create a second container for a second subset of the cloud instances, based on the similarities between the cloud instances included in the first set of cloud instances,
the second subset of the cloud instances being different from the first subset of the cloud instances.
10. The non-transitory computer-readable medium of claim 9 , wherein the one or more instructions further cause the one or more processors to:
cause the second container to execute another application instance,
the other application instance providing one or more services that correspond to services that were executed by the second subset of the cloud instances.
11. The non-transitory computer-readable medium of claim 8 , wherein the one or more instructions further cause the one or more processors to:
identify a second subset of the cloud instances based on the similarities between the cloud instances included in the first set of cloud instances,
the second subset of the cloud instances being different from the first subset of the cloud instances; and
remove the second subset of the cloud instances from the first set of cloud instances, causing the second subset of the cloud instances to cease operation.
12. The non-transitory computer-readable medium of claim 8 , wherein each application instance, of the at least one application instance, shares an operating system kernel with other application instances of the at least one application instance.
13. The non-transitory computer-readable medium of claim 8 , wherein the one or more instructions further cause the one or more processors to:
determine to create the first container based on a measure of utilization associated with similar cloud instances included in the first set of cloud instances.
14. The non-transitory computer-readable medium of claim 8 , wherein the one or more instructions, that cause the one or more processors to create the first container, cause the one or more processors to:
create the first container further based on:
predicted processor utilization output from the machine learning model, and
predicted memory utilization output from the machine learning model.
15. A method, comprising:
receiving, by a device and from a cloud computing environment, cloud instance information associated with a first set of cloud instances in the cloud computing environment;
determining, by the device and based on processing the first set of cloud instances with a machine learning model, similarities between cloud instances included in the first set of cloud instances;
creating, by the device, a first container for a first subset of the cloud instances, based on the similarities between the cloud instances included in the first set of cloud instances; and
causing, by the device, the first container to execute at least one application instance,
the at least one application instance providing one or more services that correspond to services that were executed by the first subset of the cloud instances.
16. The method of claim 15 , further comprising:
creating a second container for a second subset of the cloud instances, based on the similarities between the cloud instances included in the first set of cloud instances,
the second subset of the cloud instances being different from the first subset of the cloud instances.
17. The method of claim 16 , further comprising:
causing the second container to execute another application instance,
the other application instance providing one or more services that correspond to services that were executed by the second subset of the cloud instances.
18. The method of claim 15 , further comprising:
identifying a second subset of the cloud instances based on the similarities between the cloud instances included in the first set of cloud instances,
the second subset of the cloud instances being different from the first subset of the cloud instances; and
removing the second subset of the cloud instances from the first set of cloud instances, causing the second subset of the cloud instances to cease operation.
19. The method of claim 15 , wherein each application instance, of the at least one application instance, shares an operating system kernel with other application instances of the at least one application instance.
20. The method of claim 15 , further comprising:
determining to create the first container based on a measure of utilization associated with similar cloud instances included in the first set of cloud instances.