Systems and methods for performing a technical recovery in a cloud environment

1. A computer-implemented method for testing failover comprising:

determining risk factor data for an application operating in a first region of a cloud environment, the risk factor data including one or more cross-regional dependencies of the application to one or more applications in at least one other region of the cloud environment;

determining a risk score associated with performing failover of the application to a second region of the cloud environment by evaluating the risk factor data with a risk scoring model that includes a respective weight for each risk factor included in the risk factor data;

comparing the determined risk score with a predetermined risk score;

in response to determining that the determined risk score is lower than the predetermined risk score, performing failover of the application to the second region of the cloud environment;

isolating the second region of the cloud environment from the first region of the cloud environment for a predetermined period of time; and

monitoring operation of the application in the second region of the cloud environment during the predetermined period of time.

2. The computer-implemented method of claim 1 , wherein the risk scoring model is a trained machine learning model, whereby the respective weights have been learned via training using training risk factor data and training risk scores for one or more training applications.

3. The computer-implemented method of claim 1 , wherein the risk factor data includes one or more risk factor associated with:

a criticality of the application;

a resiliency tier of the application;

a customer impact metric;

historical information on past severity events associated with the application;

a dependency on or of the application on a lower or higher tier application;

a change frequency of the application; or

historical information on one or more past technical recovery exercise associated with the application.

4. The computer-implemented method of claim 1 , wherein the risk score of the application is determined as a weighted average of the risk factors in the risk factor data using the respective weights.

5. The computer-implemented method of claim 1 , wherein:

respective risk scores are determined for each of a plurality of applications operating in the first region of the cloud environment; and

the computer-implemented method further comprises generating a dashboard interface that includes a respective visual element indicative of the respective risk score of each application in the plurality of applications.

6. The computer-implemented method of claim 5 , further comprising:

generating an overall risk score for the plurality of applications.

7. The computer-implemented method of claim 1 , further comprising:

generating a graphical-user-interface (GUI) that includes an interactive item configured to receive a one-click instruction to execute the failover of the application, wherein the performing of the failover of the application is performed in response to receiving the one-click instruction.

8. A system for testing failover comprising:

at least one memory storing instructions and a risk scoring model this includes a respective weight for each of a plurality of risk factors; and

at least one processor operatively connected to the at least one memory, and configured to execute the instructions to perform operations, including:

determining risk factor data for an application operating in a first region of a cloud environment, the risk factor data including one or more cross-regional dependencies of the application to one or more applications in at least one other region of the cloud environment;

determining a risk score associated with performing failover of the application to a second region of the cloud environment by evaluating the risk factor data with the risk scoring model;

comparing the determined risk score with a predetermined risk score;

in response to determining that the determined risk score is lower than the predetermined risk score, performing failover of the application to the second region of the cloud environment;

isolating the second region of the cloud environment from the first region of the cloud environment for a predetermined period of time; and

monitoring operation of the application in the second region of the cloud environment during the predetermined period of time.

9. The system of claim 8 , wherein the risk scoring model is a trained machine learning model, whereby the respective weights have been learned via training using training risk factor data and training risk scores for one or more training applications.

10. The system of claim 8 , wherein the risk factor data includes one or more risk factor associated with:

a criticality of the application;

a resiliency tier of the application;

a customer impact metric;

historical information on past severity events associated with the application;

a dependency on or of the application on a lower or higher tier application;

a change frequency of the application; or

historical information on one or more past technical recovery exercise associated with the application.

11. The system of claim 8 , wherein the risk score of the application is determined as a weighted average of the risk factors in the risk factor data using the respective weights.

12. The system of claim 8 , wherein:

respective risk scores are determined for each of a plurality of applications operating in the first region of the cloud environment; and

the operations further include generating a dashboard interface that includes a respective visual element indicative of the respective risk score of each application in the plurality of applications.

13. The system of claim 12 , wherein the operations further include generating an overall risk score for the plurality of applications.

14. The system of claim 8 , wherein the operations further include generating a graphical-user-interface (GUI) that includes an interactive item configured to receive a one-click instruction to execute the failover of the application, wherein the performing of the failover of the application is performed in response to receiving the one-click instruction.

15. A non-transitory computer-readable medium comprising instructions for testing failover, the instructions being executable by at least one processor to perform operations that include:

determining risk factor data for an application operating in a first region of a cloud environment, the risk factor data including one or more cross-regional dependencies of the application to one or more applications in at least one other region of the cloud environment;

determining a risk score associated with performing failover of the application to a second region of the cloud environment by evaluating the risk factor data with a risk scoring model that includes a respective weight for each risk factor included in the risk factor data;

comparing the determined risk score with a predetermined risk score;

in response to determining that the determined risk score is lower than the predetermined risk score, performing failover of the application to the second region of the cloud environment;

isolating the second region of the cloud environment from the first region of the cloud environment for a predetermined period of time; and

monitoring operation of the application in the second region of the cloud environment during the predetermined period of time.

16. The non-transitory computer-readable medium of claim 15 , wherein the risk scoring model is a trained machine learning model, whereby the respective weights have been learned via training using training risk factor data and training risk scores for one or more training applications.

17. The non-transitory computer-readable medium of claim 15 , wherein the risk factor data includes one or more risk factor associated with:

a criticality of the application;

a resiliency tier of the application;

a customer impact metric;

historical information on past severity events associated with the application;

a dependency on or of the application on a lower or higher tier application;

a change frequency of the application; or

historical information on one or more past technical recovery exercise associated with the application.

18. The non-transitory computer-readable medium of claim 15 , wherein the risk score of the application is determined as a weighted average of the risk factors in the risk factor data using the respective weights.

19. The non-transitory computer-readable medium of claim 15 , wherein:

respective risk scores are determined for each of a plurality of applications operating in the first region of the cloud environment; and

the operations further include:

generating a dashboard interface that includes a respective visual element indicative of the respective risk score of each application in the plurality of applications; and

generating an overall risk score for the plurality of applications.

20. The non-transitory computer-readable medium of claim 15 , further comprising:

generating a graphical-user-interface (GUI) that includes an interactive item configured to receive a one-click instruction to execute the failover of the application, wherein the performing of the failover of the application is performed in response to receiving the one-click instruction.