Determining when to replace a storage device using a machine learning module
Provided are a computer program product, system, and method for using a machine learning module to determine when to replace a storage device. Input on attributes of the storage device is provided to a machine learning module to produce an output value. A determination is made whether the output value indicates to replace the storage device. Indication is made to replace the storage device in response to determining that the output value indicates to replace the storage device.
1. A computer program product for determining when to replace a storage device deployed within a computing environment, the computer program product comprising a computer readable storage medium storing computer readable program code that when executed by a processor, causes the processor to perform operations comprising:
providing input on attributes of the storage device to a machine learning module to produce an output value, wherein the attributes include an expected remaining lifespan of the storage device and at least one of: a response time to respond to read and write requests to a storage array including the storage device; a number of errors in a specified time interval for each of at least one error type; a type of the storage device; a manufacturer of the storage device; a storage capacity of the storage device; a time of first use of the storage device; a firmware level of the storage device; read operations per second at the storage device; and write operations per second at the storage device;
calculating an expected remaining life of the storage device from a function including the output value and a duration related value stored for the storage device;
determining whether the calculated expected remaining life indicates to replace the storage device;
indicating to replace the storage device in response to determining that the calculated expected remaining life indicates to replace the storage device; and
updating storage device information with the calculated expected remaining life.
2. The computer program product of claim 1 , wherein the operations further comprise:
detecting an event at the storage device, wherein the input on the attributes is provided to the machine learning module in response to detecting the event.
3. The computer program product of claim 2 , wherein the event comprises an error at the storage device.
4. The computer program product of claim 2 , wherein the event comprises detecting that a measured age of the storage device comprises a predetermined percentage of an expected lifetime of the storage device.
5. The computer program product of claim 1 , wherein the providing the input on attributes of the storage device comprises periodically providing the input to the machine learning module to periodically produce the output value based on a time interval.
6. A computer program product for determining when to replace a storage device deployed within a computing environment, the computer program product comprising a computer readable storage medium storing computer readable program code that when executed by a processor, causes the processor to perform operations comprising:
providing input on dynamic operating conditions of the storage device to a machine learning module to produce an indication of an expected remaining life of the storage device, wherein the input comprises an expected remaining lifespan of the storage device and at least one of: a response time to respond to read and write requests to a storage array including the storage device; a number of errors in a specified time interval for each of at least one error type; a type of the storage device; a manufacturer of the storage device; a storage capacity of the storage device; a time of first use of the storage device; a firmware level of the storage device; read operations per second at the storage device; and write operations per second at the storage device;
determining whether the expected remaining life indicates to replace the storage device; and
indicating to replace the storage device in response to determining that the expected remaining life indicates to replace the storage device.
7. The computer program product of claim 6 , wherein the expected remaining life comprises at least one of a time value, a number of write cycles, and number of writes per time period.
8. A system for determining when to replace a storage device, comprising:
a processor; and
a computer readable storage medium storing computer readable program code that when executed by the processor performs operations, the operations comprising:
providing input on attributes of the storage device to a machine learning module to produce an output value, wherein the attributes include an expected remaining lifespan of the storage device and at least one of: a response time to respond to read and write requests to a storage array including the storage device; a number of errors in a specified time interval for each of at least one error type; a type of the storage device; a manufacturer of the storage device; a storage capacity of the storage device; a time of first use of the storage device; a firmware level of the storage device; read operations per second at the storage device; and write operations per second at the storage device;
calculating an expected remaining life of the storage device from a function including the output value and a duration related value stored for the storage device;
determining whether the calculated expected remaining life indicates to replace the storage device;
indicating to replace the storage device in response to determining that the calculated expected remaining life indicates to replace the storage device; and
updating storage device information with the calculated expected remaining life.
9. The system of claim 8 , wherein the operations further comprise:
detecting an event at the storage device, wherein the input on the attributes is provided to the machine learning module in response to detecting the event.
10. A computer implemented method performed in a computer system for determining when to replace a storage device deployed within a computing environment, comprising:
providing input on attributes of the storage device to a machine learning module to produce an output value, wherein the attributes include an expected remaining lifespan of the storage device and at least one of: a response time to respond to read and write requests to a storage array including the storage device; a number of errors in a specified time interval for each of at least one error type; a type of the storage device; a manufacturer of the storage device; a storage capacity of the storage device; a time of first use of the storage device; a firmware level of the storage device; read operations per second at the storage device; and write operations per second at the storage device;
calculating an expected remaining life of the storage device from a function including the output value and a duration related value stored for the storage device;
determining whether the calculated expected remaining life indicates to replace the storage device;
indicating to replace the storage device in response to determining that the calculated expected remaining life indicates to replace the storage device; and
updating storage device information with the calculated expected remaining life.
11. The method of claim 10 , further comprising:
detecting an event at the storage device, wherein the input on the attributes is provided to the machine learning module in response to detecting the event.
12. The system of claim 9 , wherein the event comprises an error at the storage device.
13. The system of claim 9 , wherein the event comprises detecting that a measured age of the storage device comprises a predetermined percentage of an expected lifetime of the storage device.
14. The system of claim 8 , wherein the providing the input on attributes of the storage device comprises periodically providing the input to the machine learning module to periodically produce the output value based on a time interval.
15. A system for determining when to replace a storage device, comprising:
a processor; and
a computer readable storage medium storing computer readable program code that when executed by the processor performs operations, the operations comprising:
providing input on dynamic operating conditions of the storage device to a machine learning module to produce an indication of an expected remaining life of the storage device, wherein the input comprises an expected remaining lifespan of the storage device and at least one of: a response time to respond to read and write requests to a storage array including the storage device; a number of errors in a specified time interval for each of at least one error type; a type of the storage device; a manufacturer of the storage device; a storage capacity of the storage device; a time of first use of the storage device; a firmware level of the storage device; read operations per second at the storage device; and write operations per second at the storage device;
determining whether the expected remaining life indicates to replace the storage device; and
indicating to replace the storage device in response to determining that the expected remaining life indicates to replace the storage device.
16. The system of claim 15 , wherein the expected remaining life comprises at least one of a time value, a number of write cycles, and number of writes per time period.
17. The method of claim 11 , wherein the event comprises an error at the storage device.
18. The method of claim 11 , wherein the event comprises detecting that a measured age of the storage device comprises a predetermined percentage of an expected lifetime of the storage device.
19. The method of claim 10 , wherein the providing the input on attributes of the storage device comprises periodically providing the input to the machine learning module to periodically produce the output value based on a time interval.
20. A method for determining when to replace a storage device deployed within a computing environment, comprising:
providing input on dynamic operating conditions of the storage device to a machine learning module to produce an indication of an expected remaining life of the storage device, wherein the input comprises an expected remaining lifespan of the storage device and at least one of: a response time to respond to read and write requests to a storage array including the storage device; a number of errors in a specified time interval for each of at least one error type; a type of the storage device; a manufacturer of the storage device; a storage capacity of the storage device; a time of first use of the storage device; a firmware level of the storage device; a read operations per second at the storage device; and write operations per second at the storage device;
determining whether the expected remaining life indicates to replace the storage device; and
indicating to replace the storage device in response to determining that the expected remaining life indicates to replace the storage device.
21. The method of claim 20 , wherein the expected remaining life comprises at least one of a time value, a number of write cycles, and number of writes per time period.