SMART DATA PLACEMENT
The system obtains performance signals associated with respective hard disks of a volume of hard disks including a plurality of hard disks that are dedicated to activities of a service. The system determines a volume failure prediction for the volume of hard disks by, for each respective hard disk of the volume of hard disks, determining a hard disk failure prediction. The system determines a hard disk failure prediction by: inputting the respective performance signals into a supervised machine learning model; and receiving as output from the machine learning model the hard disk failure prediction for the respective hard disk. The system based on the received outputs, determines that the volume failure prediction is associated with a migration condition. The system, responsive to determining that the volume failure prediction is associated with the migration condition, migrates data from the volume of hard disks to a second volume of hard disks.
1 . A method comprising:
obtaining respective performance signals associated with respective hard disk drives of a collection of hard disk drives, the collection of hard disk drives comprising a plurality of hard disk drives that are dedicated to activities of a service;
determining a collection failure prediction for the collection of hard disk drives by, for each respective hard disk drive of the collection of hard disk drives, determining a hard disk drive failure prediction by:
inputting the respective performance signals into a supervised machine learning model; and
receiving, as output from the supervised machine learning model, the hard disk drive failure prediction for the respective hard disk drive;
determining, based on the received outputs, that the collection failure prediction is associated with a migration condition; and
responsive to determining that the collection failure prediction is associated with the migration condition, causing a migration of data from the collection of hard disk drives to a second collection of hard disk drives.
2 . The method of claim 1 , wherein determining the hard disk drive failure prediction further comprises:
obtaining respective performance signals associated with respective platters of a particular hard disk drive; and
for each respective platter of the particular hard disk drive:
inputting the respective performance signals associated with each respective platter into a second supervised machine learning model; and
receiving as output from the second supervised machine learning model a platter failure prediction for the respective platter.
3 . The method of claim 2 , wherein determining the hard disk drive failure prediction further comprises:
determining, based on the platter failure prediction, that the hard disk drive failure prediction is associated with a second migration condition; and
responsive to determining the hard disk drive failure prediction is associated with the second migration condition, causing a migration of data on the particular hard disk drive to a second particular hard disk drive.
4 . The method of claim 2 , wherein determining the hard disk drive failure prediction, further comprises, responsive to receiving the platter failure prediction as output, calculating a platter risk failure rating for each respective platter, the platter risk failure rating comprising a second classification of a given second plurality of candidate classifications.
5 . The method of claim 1 , wherein the collection of hard disk drives comprises a number of hard disk drives, and wherein the method further comprises:
identifying a second number of hard disk drives to be included in the second collection of hard disk drives, wherein the number of hard disk drives in the collection of hard disk drives is not equal to the second number of hard disk drives to be included in the second collection of hard disk drives.
6 . The method of claim 1 , wherein the determining the collection failure prediction, further comprises, responsive to receiving the hard disk drive failure prediction as output, calculating a risk failure rating for each respective hard disk drive, the risk failure rating comprising a classification of a given plurality of candidate classifications.
7 . The method of claim 6 , wherein determining, based on the received outputs, that the collection failure prediction is associated with the migration condition further comprises:
determining a number of respective hard disk drives which received a given classification, wherein the given classification is associated with a threshold associated with the migration condition; and
determining that the number of respective hard disk drives which received the given classification satisfies the threshold associated with the migration condition.
8 . A non-transitory computer-readable storage medium storing executable computer instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
obtaining respective performance signals associated with respective hard disk drives of a collection of hard disk drives, the collection of hard disk drives comprising a plurality of hard disk drives that are dedicated to activities of a service;
determining a collection failure prediction for the collection of hard disk drives by, for each respective hard disk drive of the collection of hard disk drives, determining a hard disk drive failure prediction by:
inputting the respective performance signals into a supervised machine learning model; and
receiving as output from the supervised machine learning model the hard disk drive failure prediction for the respective hard disk drive;
determining, based on the received outputs, that the collection failure prediction is associated with a migration condition; and
responsive to determining that the collection failure prediction is associated with the migration condition, causing a migration of data from the collection of hard disk drives to a second collection of hard disk drives.
9 . The non-transitory computer-readable storage medium of claim 8 , wherein determining the hard disk drive failure prediction further comprises:
obtaining respective performance signals associated with respective platters of a particular hard disk drive; and
for each respective platter of the particular hard disk drive:
inputting the respective performance signals associated with each respective platter into a second supervised machine learning model; and
receiving as output from the second supervised machine learning model a platter failure prediction for the respective platter.
10 . The non-transitory computer-readable storage medium of claim 9 , wherein determining the hard disk drive failure prediction further comprises:
determining, based on the platter failure prediction, that the hard disk drive failure prediction is associated with a second migration condition; and
responsive to determining the hard disk drive failure prediction is associated with the second migration condition, causing a migration of data on the particular hard disk drive to a second particular hard disk drive.
11 . The non-transitory computer-readable storage medium of claim 9 , wherein determining the hard disk drive failure prediction, further comprises, responsive to receiving the platter failure prediction as output, calculating a platter risk failure rating for each respective platter, the platter risk failure rating comprising a second classification of a given second plurality of candidate classifications.
12 . The non-transitory computer-readable storage medium of claim 8 , wherein the collection of hard disk drives comprises a number of hard disk drives, and wherein the operations further comprise:
identifying a second number of hard disk drives to be included in the second collection of hard disk drives, wherein the number of hard disk drives in the collection of hard disk drives is not equal to the second number of hard disk drives to be included in the second collection of hard disk drives.
13 . The non-transitory computer-readable storage medium of claim 8 , wherein the determining the collection failure prediction, further comprises, responsive to receiving the hard disk drive failure prediction as output, calculating a risk failure rating for each respective hard disk drive, the risk failure rating comprising a classification of a given plurality of candidate classifications.
14 . The non-transitory computer-readable storage medium of claim 13 , wherein determining, based on the received outputs, that the collection failure prediction is associated with a migration condition further comprises:
determining a number of respective hard disk drives which received a given classification, wherein the given classification is associated with a threshold associated with the migration condition; and
determining that the number of respective hard disk drives which received the given classification satisfies the threshold associated with the migration condition.
15 . A system comprising:
memory with instructions encoded thereon; and
one or more processors that, when executing the instructions, are caused to perform operations comprising:
obtaining respective performance signals associated with respective hard disk drives of a collection of hard disk drives, the collection of hard disk drives comprising a plurality of hard disk drives that are dedicated to activities of a service;
determining a collection failure prediction for the collection of hard disk drives by, for each respective hard disk drive of the collection of hard disk drives, determining a hard disk drive failure prediction by:
inputting the respective performance signals into a supervised machine learning model;
receiving as output from the supervised machine learning model the hard disk drive failure prediction for the respective hard disk drive;
determining, based on the received outputs, that the collection failure prediction is associated with a migration condition; and
responsive to determining that the collection failure prediction is associated with the migration condition, causing a migration of data from the collection of hard disk drives to a second collection of hard disk drives.
16 . The system of claim 15 , wherein determining the hard disk drive failure prediction further comprises:
obtaining respective performance signals associated with respective platters of a particular hard disk drive; and
for each respective platter of the particular hard disk drive:
inputting the respective performance signals associated with each respective platter into a second supervised machine learning model; and
receiving as output from the second supervised machine learning model a platter failure prediction for the respective platter.
17 . The system of claim 16 , wherein determining the hard disk drive failure prediction further comprises:
determining, based on the platter failure prediction, that the hard disk drive failure prediction is associated with a second migration condition; and
responsive to determining the hard disk drive failure prediction is associated with the second migration condition, causing a migration of data on the particular hard disk drive to a second particular hard disk drive.
18 . The system of claim 15 , wherein the collection of hard disk drives comprises a number of hard disk drives, and wherein the operations further comprises:
identifying a second number of hard disk drives to be included in the second collection of hard disk drives, wherein the number of hard disk drives in the collection of hard disk drives is not equal to the second number of hard disk drives to be included in the second collection of hard disk drives.
19 . The system of claim 15 , wherein determining the collection failure prediction, further comprises, responsive to receiving the hard disk drive failure prediction as output, calculating a risk failure rating for each respective hard disk drive, the risk failure rating comprising a classification of a given plurality of candidate classifications.
20 . The system of claim 19 , wherein determining, based on the received outputs, that the collection failure prediction is associated with a migration condition further comprises:
determining a number of respective hard disk drives which received a given classification, wherein the given classification is associated with a threshold associated with the migration condition; and
determining that the number of respective hard disk drives which received the given classification satisfies the threshold associated with the migration condition.