Automatically determining sizing configurations for storage components using machine learning techniques

1. A computer-implemented method comprising:

obtaining multiple items of input related to at least one storage component;

determining a set of storage component sizing configurations by processing at least a portion of the multiple items of input using a first set of one or more machine learning techniques comprising at least one deep learning technique;

identifying a subset of the storage component sizing configurations by processing at least a portion of the determined set of storage component sizing configurations using a second set of one or more machine learning techniques; and

performing one or more automated actions based at least in part on the identified subset of storage component sizing configurations;

wherein the method is performed by at least one processing device comprising a processor coupled to a memory.

2. The computer-implemented method of claim 1 , wherein the at least one deep learning technique comprises at least one neural network.

3. The computer-implemented method of claim 1 , wherein the at least one deep learning technique comprises at least one multilayer perceptron.

4. The computer-implemented method of claim 1 , wherein the one or more machine learning techniques in the second set comprises at least one machine learning-based k-nearest neighbors algorithm.

5. The computer-implemented method of claim 1 , further comprising:

generating a dynamic workload performance library by applying at least one machine learning-based unsupervised clustering model to data pertaining to multiple storage components.

6. The computer-implemented method of claim 5 , wherein the at least one machine learning-based unsupervised clustering model comprises at least one machine learning-based unsupervised clustering with Gaussian mixture model.

7. The computer-implemented method of claim 5 , wherein the data pertaining to multiple storage components comprise configuration data and feature engineering data.

8. The computer-implemented method of claim 5 , further comprising:

updating the at least one deep learning technique using the generated dynamic workload performance library.

9. The computer-implemented method of claim 1 , wherein the multiple items of inputs comprise information pertaining to two or more of input/output operations per second (IOPS), storage component capacity, at least one storage component workload, data reduction, deduplication, block size, drive type, and at least one redundant array of independent disks (RAID) group.

10. The computer-implemented method of claim 1 , wherein obtaining the multiple items of input comprises querying a storage systems database for data and applying at least one statistical analysis technique to the queried data.

11. The computer-implemented method of claim 1 , wherein performing the one or more automated actions comprises outputting the identified subset of storage component sizing configurations to a user for selection of at least one of the storage component sizing configurations from the subset.

12. A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:

to obtain multiple items of input related to at least one storage component;

to determine a set of storage component sizing configurations by processing at least a portion of the multiple items of input using a first set of one or more machine learning techniques comprising at least one deep learning technique;

to identify a subset of the storage component sizing configurations by processing at least a portion of the determined set of storage component sizing configurations using a second set of one or more machine learning techniques; and

to perform one or more automated actions based at least in part on the identified subset of storage component sizing configurations.

13. The non-transitory processor-readable storage medium of claim 12 , wherein the at least one deep learning technique comprises at least one neural network.

14. The non-transitory processor-readable storage medium of claim 12 , wherein the at least one deep learning technique comprises at least one multilayer perceptron.

15. The non-transitory processor-readable storage medium of claim 12 , wherein the one or more machine learning techniques in the second set comprises at least one machine learning-based k-nearest neighbors algorithm.

16. The non-transitory processor-readable storage medium of claim 12 , wherein the program code when executed by the at least one processing device further causes the at least one processing device:

to generate a dynamic workload performance library by applying at least one machine learning-based unsupervised clustering model to data pertaining to multiple storage components; and

to update the one or more deep learning techniques using the generated dynamic workload performance library.

17. An apparatus comprising:

at least one processing device comprising a processor coupled to a memory;

the at least one processing device being configured:

to obtain multiple items of input related to at least one storage component;

to determine a set of storage component sizing configurations by processing at least a portion of the multiple items of input using a first set of one or more machine learning techniques comprising at least one deep learning technique;

to identify a subset of the storage component sizing configurations by processing at least a portion of the determined set of storage component sizing configurations using a second set of one or more machine learning techniques; and

to perform one or more automated actions based at least in part on the identified subset of storage component sizing configurations.

18. The apparatus of claim 17 , wherein the at least one deep learning technique comprises at least one neural network.

19. The apparatus of claim 17 , wherein the at least one deep learning technique comprises at least one multilayer perceptron.

20. The apparatus of claim 17 , wherein the one or more machine learning techniques in the second set comprises at least one machine learning-based k-nearest neighbors algorithm.