Cloud resource management using externally-sourced data

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 including at least one of pictures, audio, or video from at least one source external to a cloud resource network running an application configured to host the at least one of pictures, audio, or video;

producing a score from the plurality of data samples based on at least one of sentiment analysis or emotion analysis using an artificial intelligence (AI) model and trend predictions from a trending aspect model;

generating at least one projection based on the score;

correlating the at least one projection to cluster resources for the application;

determining prospective cluster resource usage for the application based on the at least one projection;

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

receiving training data samples from the at least one source external to the cloud resource network; and

training or retraining the AI model and the trending aspect model using the training data samples.

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

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

4. The system of claim 1 , wherein the operation of adjusting the cluster resource allocation further comprises interpreting the at least one projection by referring to a usage-action based mapping table to identify the cluster resources and components to be dynamically changed, wherein the cluster resource allocation is configured to be adjusted by increasing, decreasing, or maintaining at least one of central processing unit (CPU) resource, memory, disk storage, a number of nodes assigned to at least one network cluster, a number of pods assigned to the application, or a replication factor assigned to a pod.

5. The system of claim 1 , wherein the instructions are executable for causing the processing device to perform an operation of identifying event user interest based on the at least one of pictures, audio, or video.

6. The system of claim 1 , wherein the plurality of data samples further includes text.

7. A method comprising:

receiving, by a processing device, a plurality of data samples including at least one of pictures, audio, or video from a plurality of sources external to a cloud resource network running an application configured to host the at least one of pictures, audio, or video;

producing, by the processing device, a score from the plurality of data samples based on at least one of sentiment analysis or emotion analysis using an artificial intelligence (AI) model and trend predictions from a trending aspect model;

generating, by the processing device, at least one projection based on the score;

correlating, by the processing device, the at least one projection to cluster resources for the application;

determining, by the processing device, prospective cluster resource usage for the application based on the at least one projection;

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

receiving training data samples from the plurality of sources external to the cloud resource network; and

training or retraining the AI model and the trending aspect model using the training data samples.

8. The method of claim 7 , wherein the cluster resource allocation is adjusted by increasing, decreasing, or maintaining a number of nodes allocated to at least one network cluster.

9. The method of claim 7 , 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.

10. The method of claim 7 , wherein adjusting the cluster resource allocation further comprises interpreting the at least one projection by referring to a usage-action based mapping table to identify the cluster resources and components to be dynamically changed, wherein the cluster resource allocation is configured to be adjusted by increasing, decreasing, or maintaining at least one of central processing unit (CPU) resource, memory, disk storage, a number of nodes assigned to at least one network cluster, a number of pods assigned to the application, or a replication factor assigned to a pod.

11. The method of claim 7 , further comprising identifying event user interest based on the at least one of pictures, audio, or video.

12. The method of claim 7 , wherein the plurality of data samples further includes text.

13. 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 including at least one of pictures, audio, or video from a plurality of sources external to a cloud resource network running an application configured to host the at least one of pictures, audio, or video;

produce a score from the plurality of data samples based on at least one of sentiment analysis or emotion analysis using an artificial intelligence (AI) model and trend predictions from a trending aspect model;

generate at least one projection based on the score;

correlate the at least one projection to cluster resources for the application;

determine prospective cluster resource usage for the application based on the at least one projection;

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

receive training data samples from the plurality of sources external to the cloud resource network; and

train or retrain the AI model and the trending aspect model using the training data samples.

14. The non-transitory computer-readable medium of claim 2 , wherein the cluster resource allocation is configured to be adjusted by increasing, decreasing, or maintaining a number of nodes allocated to at least one network cluster.

15. The non-transitory computer-readable medium of claim 13 , wherein the cluster resource allocation is configured to be adjusted by increasing, decreasing, or maintaining a number of pods assigned to the application, wherein each of the pods comprises at least one container.

16. The non-transitory computer-readable medium of claim 13 , further comprising program code that is executable for causing the processing device to interpret the at least one projection by referring to a usage-action based mapping table to identify the cluster resources and components to be dynamically changed, wherein the cluster resource allocation is configured to be adjusted by increasing, decreasing, or maintaining at least one of central processing unit (CPU) resource, memory, disk storage, a number of nodes assigned to at least one network cluster, a number of pods assigned to the application, or a replication factor assigned to a pod.

17. The non-transitory computer-readable medium of claim 13 , wherein the plurality of data samples further includes text.