Machine learning model for storage system
Data associated with storage media utilized by one or more storage systems is received. The data is provided as an input to a machine learning model executed by a processing device. The machine learning model identifies one or more deterministic characteristics from the data. The one or more deterministic characteristics associated with the storage media are received from the machine learning model. A data structure comprising the one or more deterministic characteristics is generated for use in a telemetry process to qualify types of storage media.
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 storage system as an input to a machine learning model executed by the processing device, wherein the machine learning model identifies one or more characteristics of the first storage media from the data;
compare one or more characteristics associated with data received by a second storage media of the storage system with the one or more characteristics of the first storage media; and
determine, by the machine learning model, a type of change associated with the second storage media based on comparing the one or more characteristics.
2. The system of claim 1 , wherein the data comprises telemetry data associated with the first storage media.
3. 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.
4. The system of claim 1 , wherein the processing device is further configured to:
provide firmware for the second storage media to the storage system, and wherein the second storage media differs from the first storage media.
5. 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 first storage media of the storage system upon determining to accept the second storage media based on the type of change.
6. 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.
7. 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.
8. A method, comprising:
providing data associated with a first storage media utilized a storage system as an input to a machine learning model executed by a processing device;
adding a second storage media to the system;
comparing one or more characteristics associated with data received by the second storage media with the one or more characteristics of the first storage media; and
determining, by the machine learning model, a type of change associated with the second storage media based on comparing the one or more characteristics.
9. The method of claim 8 , wherein the data comprises telemetry data associated with the first storage media.
10. The method of claim 8 , further comprising:
determining whether to accept or reject the second storage media based on the type of change.
11. The method of claim 8 , further comprising:
providing firmware for the second storage media to the storage system, and wherein the second storage media differs from the first storage media.
12. The method of claim 8 , further comprising:
determining whether support for the second storage media exists within firmware of the first storage media of the storage system upon determining to accept the second storage media based on the type of change.
13. The method of claim 8 , 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.
14. The method of claim 8 , further comprising:
determining whether support for the second type of storage media exists within firmware of the one or more storage systems upon determining that the new type of storage includes the second type of change.
15. The method of claim 8 , 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.
16. 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 storage system as an input to a machine learning model executed by the processing device, wherein the machine learning model identifies one or more characteristics of the first storage media from the data;
compare one or more characteristics associated with data received by a second storage media of the storage system with the one or more characteristics of the first storage media; and
determine, by the machine learning model, a type of change associated with the second storage media based on comparing the one or more characteristics.
17. The non-transitory computer readable storage medium of claim 16 , wherein the data comprises telemetry data associated with the first storage media.
18. The non-transitory computer readable storage medium of claim 16 , wherein the processing device is further configured to:
determine whether to accept or reject the second storage media based on the type of change.
19. The non-transitory computer readable storage medium of claim 16 , wherein the processing device is further configured to:
provide firmware for the second storage media to the storage system, and wherein the second storage media differs from the first storage media.
20. The non-transitory computer readable storage medium of claim 16 , 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.