Disk replacement using a predictive statistical model

Patent metadata

Field	Value
Publication number	US-9542296-B1
Application number	US-201414557374-A
Country	US
Kind code	B1
Filing date	Dec 1, 2014
Priority date	Dec 1, 2014
Publication date	Jan 10, 2017
Grant date	Jan 10, 2017

Abstract

In a provider network, attributes of one of a plurality of storage devices of the provider network are identified for failure monitoring. Based on a failure prediction model, a predicted probability of failure of the selected storage device is determined. The failure prediction model is based on historical and current data associated with failures of the storage devices of the provider network that have common attributes. The selected storage device is deactivated in response to determining that the predicted probability of failure of the selected storage device meets a criterion.

First claim

What is claimed is:

1. A computer-implemented method for managing storage devices of a provider network, the method comprising:
selecting one or more storage devices for which a predicted probability of failure is to be determined;
identifying common attributes of the selected storage devices;
accessing historical and current data associated with failure of the selected storage devices, the historical and real-time data selected based at least in part on the common attributes;
calculating the predicted probability of failure based at least in part on the historical and real-time data associated with the failure of the selected storage devices and a failure prediction model;
deactivating the selected storage devices in response to determining that the selected storage devices have a predicted probability of failure that meets one or more criteria, the one or more criteria comprising a probability of failure during a period of time indicated by the criteria; and
updating the failure prediction model as updated data associated with the failure of the storage devices becomes available.

2. The method according to claim 1 , wherein the predicted probability of failure comprises a probability distribution function.

3. The method according to claim 1 , wherein the failure prediction model comprises a weighted combination of the common attributes.

4. The method according to claim 1 , wherein the common attributes include one or more of a disk type, disk age, operating system, RAID configuration, temperature patterns, humidity patterns, and usage patterns.

5. The method according to claim 1 , further comprising replacing the selected storage devices.

6. A system configured to manage storage devices in a provider network, the system comprising:
at least one memory having stored therein computer instructions that, upon execution by one or more processors of the system, at least cause the system to:
identify attributes of one of a plurality of storage devices of the provider network for failure monitoring;
determine, based at least in part on a failure prediction model, a predicted probability of failure of the one of the plurality of storage devices, wherein the failure prediction model is based at least in part on historical and current data associated with failures of the plurality of storage devices that have common attributes; and
identify for replacement the one of the plurality of storage device in response to determining that the predicted probability of failure of the one of the plurality of storage devices meets a criterion, the predicted probability of failure being indicative of a likelihood of failure of the one of the plurality of storage devices during a threshold period of time.

7. The system of claim 6 , further comprising computer instructions that, upon execution by one or more processors of the system, at least cause the system to update the failure prediction model as updated data associated with failures become available.

8. The system of claim 7 , further comprising computer instructions that, upon execution by one or more processors of the system, at least cause the system to update the failure prediction model based on a learning function.

9. The system of claim 6 , wherein the criterion is selectable by a customer who has computing resources associated with the one of the plurality of storage devices.

10. The system of claim 9 , wherein the criterion is associated with a service level agreement associated with the customer.

11. The system of claim 6 , further comprising computer instructions that, upon execution by one or more processors of the system, at least cause the system to implement an application programming interface (API) configured to:
receive first electronic messages indicative of a request for information associated with the predicted probability of failure; and
send second electronic messages indicative of information associated with the request.

12. The system of claim 6 , wherein the predicted probability of failure comprises a probability distribution function.

13. The system of claim 6 , wherein the attributes include one or more of type, manufacturer, size, year of production, model, and duration of use of the one of the plurality of storage devices.

14. The system of claim 6 , wherein the predicted probability of failure is determined by calculating the predicted probability using a current predicted probability as an initial probability, using updated data to adjust the initial probability, and using the calculated predicted probability as the current predicted probability for a subsequent predicted probability.

15. The system of claim 6 , wherein the failure prediction model comprises a weighted combination of the attributes, wherein some attributes are weighted more based at least in part on greater expected relevance to the probability of failure.

16. The system of claim 6 , wherein the attributes include SMART data reported from the storage devices of the provider network and kernel and other log reports from host computing devices.

17. The system of claim 6 , wherein the attributes include geographic location and usage pattern of the storage devices of the provider network.

18. The system of claim 6 , wherein the criterion is a zero actual failure rate within a level of confidence.

19. The system of claim 6 , wherein at least some of the storage devices are allocated as virtual disks.

20. The system of claim 6 , further comprising computer instructions that, upon execution by one or more processors of the system, at least cause the system to provide an automated backup of the data stored on the one of the plurality of storage devices based on the predicted probability of failure.

21. A non-transitory computer-readable storage medium having stored thereon computer-readable instructions, the computer-readable instructions comprising instructions that upon execution on one or more computing devices, at least cause the one or more computing devices to:
based at least in part on a failure prediction model, determine a predicted probability of failure of a storage device that is allocated to a customer of a provider network, wherein the failure prediction model is based at least in part on past failure data associated with storage devices of the provider network that have one or more common attributes; and
allocate a different storage device to the customer in response to determining that the predicted probability of failure of the allocated storage device meets at least one service level criterion associated with the customer, the at least one service level criterion associated with the predicted probability of failure occurring within a threshold period of time.

22. The non-transitory computer-readable storage medium of claim 21 ,
wherein the predicted probability of failure is based at least in part on SMART data reported from the storage devices.

23. The non-transitory computer-readable storage medium of claim 21 , having stored thereon further computer-readable instructions that, upon execution on the one or more computing devices, at least cause the one or more computing devices to:
send a notification of the predicted probability of failure; and
receive information indicative of taking a specified action in response to the predicted probability of failure.

Assignees

• Amazon Tech Inc

Inventors

• Engers Ross Bevan
• Letz Stefan

Classifications

• G06F11/2094

  Redundant storage or storage space (G06F11/2056 takes precedence) · CPC title

• G06F11/1096

  Parity calculation or recalculation after configuration or reconfiguration of the system · CPC title

• G06F11/3452 Primary

  Performance evaluation by statistical analysis · CPC title

• G06F11/2033

  switching over of hardware resources · CPC title

• G06F11/008

  Reliability or availability analysis · CPC title