Determining when to replace a storage device by training a machine learning module
AssigneeInternational Business Machines Corporation
InventorsMatthew G. Borlick, Karl A. Nielsen, Clint A. Hardy, Lokesh M. Gupta
Life of this patent
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Abstract
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.
Claims27
1-8.
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 performs operations, the operations comprising:
- updating dynamic attributes for the storage device in response to access requests to the storage device;
- detecting a failure of the storage device;
- in response to detecting the failure of the storage device, performing:
- determining input comprising the dynamic attributes of the storage device that failed;
- and
- using the input to train a machine learning module to produce an output value indicating no expected remaining life of the storage device;
- and
- after training the machine learning module for the storage device that failed, executing the machine learning module to produce an output value based on dynamic storage attributes of an operational storage device to determine an expected remaining life of the operational storage device.
15-16. (canceled)
A system computer program product 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:
- updating dynamic attributes for the storage device in response to access requests to the storage device;
- detecting a failure of the storage device;
- in response to detecting the failure of the storage device, performing:
- determining input comprising the dynamic attributes of the storage device that failed;
- and
- using the input to train a machine learning module to produce an output value indicating no expected remaining life of the storage device;
- and
- after training the machine learning module for the storage device that failed, executing the machine learning module to produce an output value based on dynamic storage attributes of an operational storage device to determine an expected remaining life of the operational storage device.
21-22. (canceled)
A computer implemented method performed in a computer system for determining when to replace a storage device deployed within a computing environment, t comprising:
- updating dynamic attributes for the storage device in response to access requests to the storage device;
- detecting a failure of the storage device;
- in response to detecting the failure of the storage device, performing:
- determining input comprising the dynamic attributes of the storage device that failed;
- and
- using the input to train a machine learning module to produce an output value indicating no expected remaining life of the storage device;
- and
- after training the machine learning module for the storage device that failed, executing the machine learning module to produce an output value based on dynamic storage attributes of an operational storage device to determine an expected remaining life of the operational storage device.
Dependencies are inferred from the claim text.