Automated storage capacity provisioning using machine learning techniques
Methods, apparatus, and processor-readable storage media for automated storage capacity provisioning using machine learning techniques are provided herein. An example computer-implemented method includes obtaining a user-provided input comprising an identification of an amount of storage capacity to be provisioned from a storage system; determining an amount of time for which the amount of storage capacity to be provisioned will last in connection with the storage system by processing the user-provided input in connection with historical data pertaining to storage utilization using one or more machine learning techniques; outputting, to the user, the determined amount of time for which the amount of storage capacity to be provisioned will last; and performing one or more automated actions based at least in part on feedback from the user in response to the outputting of the determined amount of time for which the amount of storage capacity to be provisioned will last.
1. A computer-implemented method comprising:
obtaining a user-provided input comprising an identification of an amount of storage capacity to be provisioned from at least one storage system;
determining an amount of time for which the amount of storage capacity to be provisioned will last in connection with at least one volume by processing the user-provided input, in connection with historical data pertaining to storage utilization, using one or more machine learning techniques, wherein processing the user-provided input comprises:
generating an output modeling at least one relationship between at least a portion of the user-provided input and one or more temporal values by processing at least a portion of the user-provided input using at least one regression technique; and
determining at least one storage-related trend for at least one temporal duration by processing at least a portion of the output, generated by the at least one regression technique, using one or more machine learning-based prediction techniques;
outputting, to the user, information pertaining to the determined amount of time for which the amount of storage capacity to be provisioned will last; and
performing one or more automated actions based at least in part on feedback from the user in response to the outputting of the information pertaining to the determined amount of time for which the amount of storage capacity to be provisioned will last, wherein performing one or more automated actions comprises automatically provisioning at least a portion of the amount of storage capacity corresponding to the user-provided input;
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 performing the one or more automated actions comprises, in response to negative feedback from the user in response to the outputting of the determined amount of time for which the amount of storage capacity to be provisioned will last, querying the user for a duration of time for which an amount of storage capacity to be provisioned from the at least one storage system needs to last.
3. The computer-implemented method of claim 2 , further comprising:
determining an amount of storage capacity to be provisioned from the at least one storage system sufficient to last for the duration of time provided by the user in response to said querying, wherein determining the amount of storage capacity to be provisioned comprises processing the duration of time provided by the user in connection with historical data pertaining to storage utilization within one or more temporal constraints using the one or more machine learning techniques.
4. The computer-implemented method of claim 3 , further comprising:
outputting, to at least one storage capacity planning system, the determined amount of storage capacity to be provisioned from the at least one storage system sufficient to last for the duration of time provided by the user.
5. The computer-implemented method of claim 3 , wherein the historical data pertaining to storage utilization within one or more temporal constraints comprises historical data pertaining to storage utilization encompassing a duration of time approximate to the duration of time provided by the user.
6. The computer-implemented method of claim 1 , wherein performing the one or more automated actions comprises, in response to positive feedback from the user in response to the outputting of the determined amount of time for which the amount of storage capacity to be provisioned will last, provisioning the amount of storage capacity corresponding to the user-provided input.
7. The computer-implemented method of claim 1 , wherein performing the one or more automated actions comprises, in response to negative feedback from the user in response to the outputting of the determined amount of time for which the amount of storage capacity to be provisioned will last, querying the user for multiple durations of time for which an amount of storage capacity to be provisioned across multiple storage volumes within the at least one storage system needs to last.
8. The computer-implemented method of claim 7 , further comprising:
determining multiple amounts of storage capacity to be provisioned to the multiple storage volumes within at least one storage system sufficient to last for the multiple durations of time provided by the user in response to said querying, wherein determining the multiple amounts of storage capacity to be provisioned comprises processing the multiple durations of time provided by the user in connection with historical data pertaining to storage utilization within one or more temporal constraints using the one or more machine learning techniques.
9. 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 a user-provided input comprising an identification of an amount of storage capacity to be provisioned from at least one storage system;
to determine an amount of time for which the amount of storage capacity to be provisioned will last in connection with at least one volume by processing the user-provided input, in connection with historical data pertaining to storage utilization, using one or more machine learning techniques, wherein processing the user-provided input comprises:
generating an output modeling at least one relationship between at least a portion of the user-provided input and one or more temporal values by processing at least a portion of the user-provided input using at least one regression technique; and
determining at least one storage-related trend for at least one temporal duration by processing at least a portion of the output, generated by the at least one regression technique, using one or more machine learning-based prediction techniques;
to output, to the user, information pertaining to the determined amount of time for which the amount of storage capacity to be provisioned will last; and
to perform one or more automated actions based at least in part on feedback from the user in response to the outputting of the information pertaining to the determined amount of time for which the amount of storage capacity to be provisioned will last, wherein performing one or more automated actions comprises automatically provisioning at least a portion of the amount of storage capacity corresponding to the user-provided input.
10. The non-transitory processor-readable storage medium of claim 9 , wherein performing the one or more automated actions comprises, in response to negative feedback from the user in response to the outputting of the determined amount of time for which the amount of storage capacity to be provisioned will last, querying the user for a duration of time for which an amount of storage capacity to be provisioned from the at least one storage system needs to last.
11. The non-transitory processor-readable storage medium of claim 10 , wherein the program code when executed by the at least one processing device causes the at least one processing device:
to determine an amount of storage capacity to be provisioned from the at least one storage system sufficient to last for the duration of time provided by the user in response to said querying, wherein determining the amount of storage capacity to be provisioned comprises processing the duration of time provided by the user in connection with historical data pertaining to storage utilization within one or more temporal constraints using the one or more machine learning techniques.
12. The non-transitory processor-readable storage medium of claim 9 , wherein performing the one or more automated actions comprises, in response to positive feedback from the user in response to the outputting of the determined amount of time for which the amount of storage capacity to be provisioned will last, provisioning the amount of storage capacity corresponding to the user-provided input.
13. The non-transitory processor-readable storage medium of claim 9 , wherein performing the one or more automated actions comprises, in response to negative feedback from the user in response to the outputting of the determined amount of time for which the amount of storage capacity to be provisioned will last, querying the user for multiple durations of time for which an amount of storage capacity to be provisioned across multiple storage volumes within the at least one storage system needs to last.
14. The non-transitory processor-readable storage medium of claim 13 , wherein the program code when executed by the at least one processing device causes the at least one processing device:
to determine multiple amounts of storage capacity to be provisioned to the multiple storage volumes within at least one storage system sufficient to last for the multiple durations of time provided by the user in response to said querying, wherein determining the multiple amounts of storage capacity to be provisioned comprises processing the multiple durations of time provided by the user in connection with historical data pertaining to storage utilization within one or more temporal constraints using the one or more machine learning techniques.
15. 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 a user-provided input comprising an identification of an amount of storage capacity to be provisioned from at least one storage system;
to determine an amount of time for which the amount of storage capacity to be provisioned will last in connection with at least one volume by processing the user-provided input, in connection with historical data pertaining to storage utilization, using one or more machine learning techniques, wherein processing the user-provided input comprises:
generating an output modeling at least one relationship between at least a portion of the user-provided input and one or more temporal values by processing at least a portion of the user-provided input using at least one regression technique; and
determining at least one storage-related trend for at least one temporal duration by processing at least a portion of the output, generated by the at least one regression technique, using one or more machine learning-based prediction techniques;
to output, to the user, information pertaining to the determined amount of time for which the amount of storage capacity to be provisioned will last; and
to perform one or more automated actions based at least in part on feedback from the user in response to the outputting of the information pertaining to the determined amount of time for which the amount of storage capacity to be provisioned will last, wherein performing one or more automated actions comprises automatically provisioning at least a portion of the amount of storage capacity corresponding to the user-provided input.
16. The apparatus of claim 15 , wherein performing the one or more automated actions comprises, in response to negative feedback from the user in response to the outputting of the determined amount of time for which the amount of storage capacity to be provisioned will last, querying the user for a duration of time for which an amount of storage capacity to be provisioned from the at least one storage system needs to last.
17. The apparatus of claim 16 , wherein the at least one processing device being further configured:
to determine an amount of storage capacity to be provisioned from the at least one storage system sufficient to last for the duration of time provided by the user in response to said querying, wherein determining the amount of storage capacity to be provisioned comprises processing the duration of time provided by the user in connection with historical data pertaining to storage utilization within one or more temporal constraints using the one or more machine learning techniques.
18. The apparatus of claim 15 , wherein performing the one or more automated actions comprises, in response to positive feedback from the user in response to the outputting of the determined amount of time for which the amount of storage capacity to be provisioned will last, provisioning the amount of storage capacity corresponding to the user-provided input.
19. The apparatus of claim 15 , wherein performing the one or more automated actions comprises, in response to negative feedback from the user in response to the outputting of the determined amount of time for which the amount of storage capacity to be provisioned will last, querying the user for multiple durations of time for which an amount of storage capacity to be provisioned across multiple storage volumes within the at least one storage system needs to last.
20. The apparatus of claim 19 , wherein the at least one processing device being further configured:
to determine multiple amounts of storage capacity to be provisioned to the multiple storage volumes within at least one storage system sufficient to last for the multiple durations of time provided by the user in response to said querying, wherein determining the multiple amounts of storage capacity to be provisioned comprises processing the multiple durations of time provided by the user in connection with historical data pertaining to storage utilization within one or more temporal constraints using the one or more machine learning techniques.