Optimized machine learning telemetry processing for a cloud based storage system

1. A system comprising:

a memory; and

a processing device, operatively coupled to the memory, the processing device configured to:

provide data associated with a first storage media of a cloud-based storage system as an input to a machine learning model that identifies one or more characteristics of the first storage media from the data;

determine, by the machine learning model, a type of change associated with a second storage media based on a comparison of the one or more characteristics of the first storage media and one or more characteristics of the second storage media; and

provide the type of change associated with the second storage media to a cloud services provider of the cloud-based storage system.

2. The system of claim 1 , wherein the processing device is further configured to:

provide the one or more characteristics of the first storage media and the one or more characteristics of the second storage media to the cloud services provider.

3. The system of claim 1 , wherein the data comprises telemetry data associated with the first storage media.

4. The system of claim 3 , wherein the processing device is further configured to:

provide the data comprising the telemetry data associated with the first storage media to the cloud services provider.

5. The system of claim 1 , wherein the processing device is further configured to:

determine whether to accept or reject the second storage media based on the type of change.

6. The system of claim 5 , wherein the processing device is further configured to:

provide an indication of a result of the determination of whether to accept or reject the second storage media to the cloud services provider.

7. The system of claim 1 , wherein the one or more characteristics comprise at least one of one of error rates, data retention times, modes of failure, read disturb counts, number of program/erase cycles, temperature, powered-on/powered-off times, latencies.

8. The system of claim 1 , wherein the processing device is further configured to:

determine whether support for the second storage media exists within firmware of the storage system upon based on performance parameters associated received from the machine learning model.

9. A method, comprising:

providing data associated with a first storage media of a cloud-based storage system as an input to a machine learning model executed by a processing device that identifies one or more characteristics of the first storage media from the data;

determining, by the machine learning model, a type of change associated with a second storage media based on a comparison of the one or more characteristics of the first storage media and one or more characteristics of the second storage media; and

providing the type of change associated with the second storage media to a cloud services provider of the cloud-based storage system.

10. The method of claim 9 , further comprising:

providing the one or more characteristics of the first storage media and the one or more characteristics of the second storage media to the cloud services provider.

11. The method of claim 9 , wherein the data comprises telemetry data associated with the first storage media.

12. The method of claim 11 , further comprising:

providing the data comprising the telemetry data associated with the first storage media to the cloud services provider.

13. The method of claim 9 , further comprising:

determining whether to accept or reject the second storage media based on the type of change.

14. The method of claim 13 , further comprising:

providing an indication of a result of the determination of whether to accept or reject the second storage media to the cloud services provider.

15. The method of claim 9 , wherein the one or more characteristics comprise at least one of one of error rates, data retention times, modes of failure, read disturb counts, number of program/erase cycles, temperature, powered-on/powered-off times, latencies.

16. The method of claim 9 , further comprising:

determining whether support for the second storage media exists within firmware of the storage system upon based on performance parameters associated received from the machine learning model.

17. A non-transitory computer readable storage medium storing instructions which, when executed, cause a processing device to:

provide data associated with a first storage media of a cloud-based storage system as an input to a machine learning model that identifies one or more characteristics of the first storage media from the data;

determine, by the machine learning model, a type of change associated with a second storage media based on a comparison of the one or more characteristics of a first storage media and one or more characteristics of a second storage media; and

provide the type of change associated with the second storage media to a cloud services provider of the cloud-based storage system.

18. The non-transitory computer readable storage medium of claim 17 , wherein the processing device is further configured to:

provide the one or more characteristics of the first storage media and the one or more characteristics of the second storage media to the cloud services provider.

19. The non-transitory computer readable storage medium of claim 17 , wherein the data comprises telemetry data associated with the first storage media.

20. The non-transitory computer readable storage medium of claim 19 , wherein the processing device is further configured to:

provide the data comprising the telemetry data associated with the first storage media to the cloud services provider.