SMART PATCH RISK PREDICTION AND VALIDATION FOR LARGE SCALE DISTRIBUTED INFRASTRUCTURE

1 . A method for implementing a change to a plurality of devices in a computing infrastructure, the method comprising:

generating a risk prediction model, the risk prediction model trained using a combination of supervised learning and unsupervised learning;

identifying, using the risk prediction model, a first set of devices from the plurality of devices having a low risk of failure due to implementing the change and a second set of devices from the plurality of devices having a high risk of failure due to implementing the change;

generating a schedule for automatically implementing the change to the first set of devices;

implementing the change to a portion of the first set of devices according to the schedule;

updating the risk prediction model using data obtained from implementing the change to the portion of the first set of devices; and

iteratively performing the identifying, the generating, the implementing, and the updating.

2 . The method as in claim 1 , wherein the change to the plurality of devices includes a software patch to a plurality of computing devices.

3 . The method as in claim 1 , wherein generating the risk prediction model comprises:

collecting historic data on previous changes implemented on the plurality of devices;

implementing the change to a test group of the plurality of devices;

identifying failed devices from the test group where implementing the change failed to be implemented; and

inputting data from the failed devices to the risk prediction model.

4 . The method as in claim 3 , wherein generating the risk prediction model further comprises:

monitoring metrics for the test group of the plurality of devices where implementing the change failed;

correlating deviations in the metrics with configuration data for the test group of the plurality of devices where implementing the change failed; and

determining a causal relationship between the change and the metrics.

5 . The method as in claim 1 , wherein the unsupervised learning includes clustering data to include risk indicator features for generating the risk prediction model.

6 . The method as in claim 3 , wherein the supervised learning includes historic data and data from implementing the change to the test group.

7 . A computer program product for implementing a change to a plurality of devices in a computing infrastructure, the computer program product being tangibly embodied on a non-transitory computer-readable medium and including executable code that, when executed, is configured to cause at least one computing device to:

generate a risk prediction model, the risk prediction model trained using a combination of supervised learning and unsupervised learning;

identify, using the risk prediction model, a first set of devices from the plurality of devices having a low risk of failure due to implementing the change and a second set of devices from the plurality of devices having a high risk of failure due to implementing the change;

generate a schedule for automatically implementing the change to the first set of devices;

implement the change to a portion of the first set of devices according to the schedule;

update the risk prediction model using data obtained from implementing the change to the portion of the first set of devices; and

iteratively perform the identifying, the generating, the implementing, and the updating.

8 . The computer program product of claim 7 , wherein the change to the plurality of devices includes a software patch to a plurality of computing devices.

9 . The computer program product of claim 7 , wherein generating the risk prediction model comprises executable code that, when executed, is configured to cause the at least one computing device to:

collect historic data on previous changes implemented on the plurality of devices;

implement the change to a test group of the plurality of devices;

identify failed devices from the test group where implementing the change failed to be implemented; and

input data from the failed devices to the risk prediction model.

10 . The computer program product of claim 9 , wherein generating the risk prediction model further comprises executable code that, when executed, is configured to cause the at least one computing device to:

monitor metrics for the test group of the plurality of devices where implementing the change failed;

correlate deviations in the metrics with configuration data for the test group of the plurality of devices where implementing the change failed; and

determine a causal relationship between the change and the metrics.

11 . The computer program product of claim 7 , wherein the unsupervised learning includes clustering data to include risk indicator features for generating the risk prediction model.

12 . The computer program product of claim 9 , wherein the supervised learning includes historic data and data from implementing the change to the test group.

13 . A system for implementing a change to a plurality of devices in a computing infrastructure comprising:

at least one processor; and

a memory storing instructions that, when executed by the at least one processor, causes the at least one processor to:

generate a risk prediction model, the risk prediction model trained using a combination of supervised learning and unsupervised learning;

identify, using the risk prediction model, a first set of devices from the plurality of devices having a low risk of failure due to implementing the change and a second set of devices from the plurality of devices having a high risk of failure due to implementing the change;

generate a schedule for automatically implementing the change to the first set of devices;

implement the change to a portion of the first set of devices according to the schedule;

update the risk prediction model using data obtained from implementing the change to the portion of the first set of devices; and

iteratively perform the identifying, the generating, the implementing, and the updating.

14 . The system of claim 13 , wherein the change to the plurality of devices includes a software patch to a plurality of computing devices.

15 . The system of claim 13 , wherein generating the risk prediction model comprises instructions that, when executed, is configured to cause the at least one processor to:

collect historic data on previous changes implemented on the plurality of devices;

implement the change to a test group of the plurality of devices;

identify failed devices from the test group where implementing the change failed to be implemented; and

input data from the failed devices to the risk prediction model.

16 . The system of claim 15 , wherein generating the risk prediction model further comprises instructions that, when executed, is configured to cause the at least one processor to:

monitor metrics for the test group of the plurality of devices where implementing the change failed;

correlate deviations in the metrics with configuration data for the test group of the plurality of devices where implementing the change failed; and

determine a causal relationship between the change and the metrics.

17 . The system of claim 13 , wherein the unsupervised learning includes clustering data to include risk indicator features for generating the risk prediction model.

18 . The system of claim 15 , wherein the supervised learning includes historic data and data from implementing the change to the test group.