Efficient management of optimal read levels for flash storage systems
Tuning information associated with a storage device of a plurality of storage devices is received. One or more characteristics associated with the storage device are determined. The tuning information and the one or more characteristics are provided to the plurality of storage devices, wherein providing the tuning information causes a set of the plurality of storage devices to apply the tuning information based on the one or more characteristics.
1. A storage system comprising:
a plurality of storage devices; and
a storage controller operatively coupled to one or more other storage devices of the plurality of storage devices, each storage device of the plurality of storage devices comprising a respective controller and the storage controller comprising a processing device, the processing device to:
receive tuning information associated with a first storage device of the plurality of storage devices;
determine one or more characteristics associated with the first storage device; and
provide the tuning information to one or more other storage devices of the plurality of storage devices comprising the one or more characteristics, wherein providing the tuning information causes the one or more other storage devices of the plurality of storage devices to use the tuning information for data storage and retrieval based on the one or more characteristics.
2. The storage system of claim 1 , wherein the tuning information comprises voltage threshold tuning for one or more memory cells of the first storage device.
3. The storage system of claim 1 , wherein the storage controller is a central storage controller that is external to the plurality of storage devices.
4. The storage system of claim 1 , wherein the storage controller is a local storage controller of the plurality of storage devices.
5. The storage system of claim 1 , wherein the processing device is further to:
provide the tuning information and the one or more characteristics as inputs to a machine learning model, the machine learning model to determine optimized tuning information for storage devices based on corresponding characteristics of the plurality of storage devices.
6. The storage system of claim 5 , wherein the processing device is further to:
identify a new storage device to be added to the storage system;
receive, from the machine learning model, the optimized tuning information for the new storage device; and
cause the new storage device to apply the optimized tuning information.
7. The storage system of claim 5 , wherein the machine learning model is executed by a cloud computing system.
8. A method, comprising:
receiving tuning information associated with a first storage device of a plurality of storage devices, wherein each storage device of the plurality of storage devices comprises a respective controller;
determining one or more characteristics associated with the first storage device; and
providing, by a processing device of a storage controller, the tuning information to one or more other storage devices of the plurality of storage devices comprising the one or more characteristics, wherein providing the tuning information causes the one or more other storage devices-of the plurality of storage devices to apply the tuning information for data storage and retrieval based on the one or more characteristics.
9. The method of claim 8 , wherein the tuning information comprises voltage threshold tuning for one or more memory cells of the first storage device.
10. The method of claim 8 , wherein the storage controller is a central storage controller that is external to the plurality of storage devices.
11. The method of claim 8 , wherein the storage controller is a local storage controller of the first storage device.
12. The method of claim 8 , further comprising:
providing the tuning information and the one or more characteristics as inputs to a machine learning model, the machine learning model to determine optimized tuning information for storage devices based on corresponding characteristics of the plurality of storage devices.
13. The method of claim 12 , further comprising:
identifying a new storage device to be added to a storage system;
receiving, from the machine learning model, the optimized tuning information for the new storage device; and
causing the new storage device to apply the optimized tuning information.
14. The method of claim 12 , wherein the machine learning model is executed by a cloud computing system.
15. A non-transitory computer readable storage medium storing instructions, which when executed, cause a processing device of a storage controller to:
receive tuning information associated with a first storage device of a plurality of storage devices, wherein each storage device of the plurality of storage devices comprises a respective controller;
determine one or more characteristics associated with the first storage device; and
provide, by the processing device of the storage controller, the tuning information to one or more other storage devices of the plurality of storage devices comprising the one or more characteristics, wherein providing the tuning information causes the one or more other storage devices of the plurality of storage devices to apply the tuning information for data storage and retrieval based on the one or more characteristics.
16. The non-transitory computer readable storage medium of claim 15 , wherein the tuning information comprises voltage threshold tuning for one or more memory cells of the first storage device.
17. The non-transitory computer readable storage medium of claim 15 , wherein the storage controller is a central storage controller that is external to the plurality of storage devices.
18. The non-transitory computer readable storage medium of claim 15 , wherein the storage controller is a local storage controller of the first storage device.
19. The non-transitory computer readable storage medium of claim 15 , wherein the processing device is further to:
provide the tuning information and the one or more characteristics as inputs to a machine learning model, the machine learning model to determine optimized tuning information for storage devices based on corresponding characteristics of the plurality of storage devices.
20. The non-transitory computer readable storage medium of claim 19 , wherein the processing device is further to:
identify a new storage device to be added to a storage system;
receive, from the machine learning model, the optimized tuning information for the new storage device; and
cause the new storage device to apply the optimized tuning information.