Automatically allocating device resources using machine learning techniques
Methods, apparatus, and processor-readable storage media for automatically allocating device resources using machine learning techniques are provided herein. An example computer-implemented method includes determining values for multiple parameters from data obtained from one or more devices; generating at least one device component failure prediction by applying one or more machine learning techniques to at least a portion of the determined values; computing one or more qualifying values attributable to the at least one generated device component failure prediction by providing the at least one generated device component failure prediction to a conformal prediction framework; and automatically allocating one or more resources to at least a portion of the one or more devices based at least in part on the at least one generated device component failure prediction and the one or more computed qualifying values.
1. A computer-implemented method comprising:
determining values for multiple parameters from data obtained from one or more devices;
generating at least one device component failure prediction by applying one or more machine learning techniques to at least a portion of the determined values;
computing one or more qualifying values attributable to the at least one generated device component failure prediction by providing the at least one generated device component failure prediction to a conformal prediction framework, wherein the conformal prediction framework comprises an inductive conformal prediction framework; and
automatically allocating one or more resources to at least a portion of the one or more devices based at least in part on the at least one generated device component failure prediction and the one or more computed qualifying values;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.
2. The computer-implemented method of claim 1 , further comprising:
ranking each device component identified in the at least one generated device component failure prediction based at least in part on the one or more computed qualifying values.
3. The computer-implemented method of claim 2 , further comprising:
ranking a set of resources based at least in part on a level of interaction between each resource in the set of resources and each device component identified in the at least one generated device component failure prediction.
4. The computer-implemented method of claim 3 , wherein automatically allocating the one or more resources comprises automatically allocating one or more resources from the set of resources based at least in part on the ranking of each device component identified in the at least one generated device component failure prediction and the ranking of the set of resources.
5. The computer-implemented method of claim 1 , wherein the one or more machine learning techniques comprises at least one stochastic gradient descent classification algorithm.
6. The computer-implemented method of claim 1 , wherein the one or more machine learning techniques comprises at least one stochastic gradient descent classification algorithm integrated with at least one isotonic regression algorithm.
7. The computer-implemented method of claim 1 , further comprising:
identifying the multiple parameters from a set of parameters by applying multiple machine learning techniques to a dataset related to device component failure.
8. The computer-implemented method of claim 7 , wherein the multiple machine learning techniques comprise at least two of: one or more gradient boosting algorithms, one or more extra trees algorithms, one or more random forest algorithms, one or more decision tree algorithms, and one or more adaptive boosting algorithms.
9. The computer-implemented method of claim 1 , wherein the one or more devices comprise multiple storage devices, wherein the one or more resources comprises one or more spare disks, and wherein the computer-implemented method comprises:
generating predictions pertaining to which of the multiple storage devices are going to fail by applying the one or more machine learning techniques to at least a portion of the determined values;
computing confidence values attributable to the storage devices predicted to fail by providing the predictions to the conformal prediction framework;
ranking the storage devices predicted to fail based at least in part on the computed confidence values;
determining one or more redundant array of independent disk groups to which the disks predicted to fail belong;
ranking the one or more redundant array of independent disk groups based at least in part on the disks predicted to fail contained therein; and
automatically allocating one or more spare disks from a spare storage pool to at least one hot spare associated with the storage devices predicted to fail based at least in part on the ranking of the storage devices predicted to fail and the ranking of the one or more redundant array of independent disk groups.
10. The computer-implemented method of claim 1 , wherein the one or more qualifying values comprise at least one confidence value attributed to the at least one generated device component failure prediction.
11. The computer-implemented method of claim 1 , wherein the one or more qualifying values comprise at least one credibility value indicating quality of the data obtained from the one or more devices used in generating the at least one generated device component failure prediction.
12. A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:
to determine values for multiple parameters from data obtained from one or more devices;
to generate at least one device component failure prediction by applying one or more machine learning techniques to at least a portion of the determined values;
to compute one or more qualifying values attributable to the at least one generated device component failure prediction by providing the at least one generated device component failure prediction to a conformal prediction framework, wherein the conformal prediction framework comprises an inductive conformal prediction framework; and
to automatically allocate one or more resources to at least a portion of the one or more devices based at least in part on the at least one generated device component failure prediction and the one or more computed qualifying values.
13. The non-transitory processor-readable storage medium of claim 12 , wherein the program code when executed by the at least one processing device further causes the at least one processing device:
to rank each device component identified in the at least one generated device component failure prediction based at least in part on the one or more computed qualifying values.
14. The non-transitory processor-readable storage medium of claim 13 , wherein the program code when executed by the at least one processing device further causes the at least one processing device:
to rank a set of resources based at least in part on a level of interaction between each resource in the set of resources and each device component identified in the at least one generated device component failure prediction.
15. The non-transitory processor-readable storage medium of claim 14 , wherein automatically allocating the one or more resources comprises automatically allocating one or more resources from the set of resources based at least in part on the ranking of each device component identified in the at least one generated device component failure prediction and the ranking of the set of resources.
16. An apparatus comprising:
at least one processing device comprising a processor coupled to a memory;
the at least one processing device being configured:
to determine values for multiple parameters from data obtained from one or more devices;
to generate at least one device component failure prediction by applying one or more machine learning techniques to at least a portion of the determined values;
to compute one or more qualifying values attributable to the at least one generated device component failure prediction by providing the at least one generated device component failure prediction to a conformal prediction framework, wherein the conformal prediction framework comprises an inductive conformal prediction framework; and
to automatically allocate one or more resources to at least a portion of the one or more devices based at least in part on the at least one generated device component failure prediction and the one or more computed qualifying values.
17. The apparatus of claim 16 , the at least one processing device being further configured:
to rank each device component identified in the at least one generated device component failure prediction based at least in part on the one or more computed qualifying values.
18. The apparatus of claim 17 , the at least one processing device being further configured:
to rank a set of resources based at least in part on a level of interaction between each resource in the set of resources and each device component identified in the at least one generated device component failure prediction.
19. The apparatus of claim 18 , wherein automatically allocating the one or more resources comprises automatically allocating one or more resources from the set of resources based at least in part on the ranking of each device component identified in the at least one generated device component failure prediction and the ranking of the set of resources.
20. The apparatus of claim 16 , wherein the one or more machine learning techniques comprises at least one stochastic gradient descent classification algorithm integrated with at least one isotonic regression algorithm.