CLOUD RESOURCE MANAGEMENT USING EXTERNALLY-SOURCED DATA

• US 20200136987A1
• Filed: 10/30/2018
• Published: 04/30/2020
• Est. Priority Date: 10/30/2018
• Status: Active Grant

• Current Assignee: Red Hat, Inc. (International Business Machines Corporation)
• Original Assignee: Red Hat, Inc. (International Business Machines Corporation)
• Inventors: Nakfour, Juana
• Granted Patent: US 11,159,446 B2
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04L 41/16 using machine learning or artificial intelligence
• CPC Class Codes: H04L 41/5054 Automatic deployment of services triggered by the service manager, e.g. service implementation by automatic configuration of network components
• CPC Class Codes: H04L 41/5096 wherein the managed service relates to distributed or central networked applications
• CPC Class Codes: H04L 47/70 Admission control; Resource allocation
• CPC Class Codes: H04L 47/83 based on usage prediction
• 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 67/1097 for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]

Abstract

A processing device can make use of an Artificial Intelligence (AI) model to make projections about resource usage based on data from external sources. The processing device can then adjust cloud-based resources accordingly. The cluster resource allocation can be adjusted as examples, by increasing, decreasing, or maintaining a number of nodes allocated to a cluster, by increasing, decreasing, or maintaining the number of pods assigned to an application or a container running in a cluster, or by increasing, decreasing, or maintaining central processing unit (CPU) resource, memory, disk storage, or a replication factor assigned to a pod.

Claims

1. A system comprising:

a processing device; and

a memory device including instructions that are executable by the processing device for causing the processing device to perform operations comprising;

receiving a plurality of data samples from a plurality of external sources;

generating at least one projection based on the plurality of data samples using an artificial intelligence (AI) model;

determining prospective cluster resource usage for an application running on at least one cluster of a cloud resource network based on the at least one projection; and

adjusting a cluster resource allocation for the application in accordance with the prospective cluster resource usage.

2. The system of claim 1 wherein the operations further comprise:

receiving training data from at least some of the plurality of external sources; and

training the artificial intelligence (AI) model using the training data.

3. The system of claim 1 wherein the cluster resource allocation is adjusted by increasing, decreasing, or maintaining a number of nodes allocated to the at least one cluster.

4. The system of claim 1 wherein cluster resource allocation is adjusted by increasing, decreasing, or maintaining a number of pods assigned to the application, wherein each of the pods comprises at least one container.

5. The system of claim 1 wherein the cluster resource allocation is adjusted by increasing, decreasing, or maintaining at least one of central processing unit (CPU) resource, memory, disk storage, or replication factor assigned to a pod.

6. The system of claim 1 wherein the data samples comprise at least one of event data, social media data, or environment data.

7. The system of claim 1 wherein the artificial intelligence (AI) model comprises a text analytics model, and wherein the at least one projection is generated based on a sentiment score produced using the text analytics model.

8. A method comprising:

receiving, by a processing device, a plurality of data samples from a plurality of external sources;

generating, by the processing device, at least one projection based on the plurality of data samples using an artificial intelligence (AI) model;

determining, by the processing device, prospective cluster resource usage for an application running on at least one cluster of a cloud resource network based on the at least one projection; and

adjusting, by the processing device, a cluster resource allocation for the application in accordance with the prospective cluster resource usage.

9. The method of claim 8 further comprising:

receiving training data from at least some of the plurality of external sources; and

training the artificial intelligence (AI) model using the training data.

10. The method of claim 8 wherein the cluster resource allocation is adjusted by increasing, decreasing, or maintaining a number of nodes allocated to the at least one cluster.

11. The method of claim 8 wherein the cluster resource allocation is adjusted by increasing, decreasing, or maintaining a number of pods assigned to the application, wherein each of the pods comprises at least one container.

12. The method of claim 8 wherein the cluster resource allocation is adjusted by increasing, decreasing, or maintaining at least one of central processing unit (CPU) resource, memory, disk storage, or replication factor assigned to a pod.

13. The method of claim 8 wherein the data samples comprise at least one of event data, social media data, or environment data.

14. The method of claim 8 wherein the artificial intelligence (AI) model comprises a text analytics model, and wherein the at least one projection is generated based on a sentiment score produced using the text analytics model.

15. A non-transitory computer-readable medium comprising program code that is executable by a processing device for causing the processing device to:

receive a plurality of data samples from a plurality of external sources;

generate at least one projection based on the plurality of data samples using an artificial intelligence (AI) model;

determine prospective cluster resource usage for an application running on at least one cluster of a cloud resource network based on the at least one projection; and

adjust a cluster resource allocation for the application in accordance with the prospective cluster resource usage.

16. The non-transitory computer-readable medium of claim 15 wherein program code is further executable by the processing device for causing the processing device to:

receive training data from at least some of the plurality of external sources; and

train the artificial intelligence (AI) model using the training data.

17. The non-transitory computer-readable medium of claim 15 wherein the cluster resource allocation is adjusted by increasing, decreasing, or maintaining a number of nodes allocated to the at least one cluster.

18. The non-transitory computer-readable medium of claim 15 wherein the cluster resource allocation is adjusted by increasing, decreasing, or maintaining a number of pods assigned to the application, wherein each of the pods comprises at least one container.

19. The non-transitory computer-readable medium of claim 15 wherein the cluster resource allocation is adjusted by increasing, decreasing, or maintaining at least one of central processing unit (CPU) resource, memory, disk storage, or replication factor assigned to a pod.

20. The non-transitory computer-readable medium of claim 15 wherein the artificial intelligence (AI) model comprises a text analytics model, and wherein the at least one projection is generated based on a sentiment score produced using the text analytics model.