Granted Patent B2

US 12,386,679 · App. 17/543,938 · Granted Aug 12, 2025

Inventors: Sunil Narang (Glen Allen, VA); Jean Muskatel (Bethesda, MD); Kathleen Poeter (Richmond, VA); Nicholas Bhaskar (Carnegie, PA); Nazia Sarang (Henrico, VA)

Assignee: Capital One Services, LLC

G06F9/5088G06F9/50G06F9/5072

Abstract

Claims (44)

1. A computer-implemented method for adaptive reserving of computing instances in a cloud-based computing system, the computer-implemented method comprising:

determining, by one or more computing devices, a first simulated usage capacity of a first set of applications executing in a first zone of the cloud-based computing system at an initial time using a machine learning model;

determining, by the one or more computing devices, a first amount of cloud-based computing instances in a second zone of the cloud-based computing system needed to maintain the first simulated usage capacity in an event of a failover of the first zone;

determining, by the one or more computing devices, a second simulated usage capacity of a second set of applications executing in the first zone at a later time than the initial time using the machine learning model, wherein the second set of applications is different from the first set of applications;

determining, by the one or more computing devices, a second amount of cloud-based computing instances in the second zone needed to maintain the first and second simulated usage capacities in the event of a failover of the first zone;

reserving, by the one or more computing devices, the second amount of cloud-based computing instances in the second zone when the second amount is less than the first amount; and

dynamically modifying the reserved second amount of cloud-based computing instances in the second zone when the reserved amount does not align with current data trends continuously provided from the machine learning model.

2. The computer-implemented method of claim 1 , wherein the reserving comprises generating, by the one or more computing devices, a capacity reservation request to reserve the second amount of cloud-based computing instances in the second zone.

3. The computer-implemented method of claim 2 , wherein the reserving further comprises transmitting, by the one or more computing devices, the request to a capacity reservation service of the cloud-based computing system.

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

the first zone is distributed across a first geographic region; and

the second zone is distributed across a second geographic region different from the first geographic region.

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

reserving, by the one or more computing devices, the first amount of cloud-based computing instances in the second zone.

6. A non-transitory computer readable medium including instructions for causing a processor to perform operations for adaptive reserving of computing instances in a cloud-based computing system, the operations comprising:

determining a first simulated usage capacity of a first set of applications executing in a first zone of the cloud-based computing system at an initial time using a machine learning model;

determining a first amount of cloud-based computing instances in a second zone of the cloud-based computing system needed to maintain the first simulated usage capacity in an event of a failover of the first zone;

determining a second simulated usage capacity of a second set of applications executing in the first zone at a later time than the initial time using the machine learning model, wherein the second set of applications is different from the first set of applications;

determining a second amount of cloud-based computing instances in the second zone needed to maintain the first and second simulated usage capacities in the event of a failover of the first zone;

reserving the second amount of cloud-based computing instances in the second zone when the second amount is less than the first amount; and

dynamically modifying the reserved second amount of cloud-based computing instances in the second zone when the reserved amount does not align with current data trends continuously provided from the machine learning model.

7. The non-transitory computer readable medium of claim 6 , wherein to perform the reserving, the operations comprise generating a capacity reservation request to reserve the second amount of cloud-based computing instances in the second zone.

8. The non-transitory computer readable medium of claim 6 , wherein to perform the reserving, the operations further comprise transmitting the request to a capacity reservation service of the cloud-based computing system.

9. The non-transitory computer readable medium of claim 6 , wherein:

the first zone is distributed across a first geographic region; and

the second zone is distributed across a second geographic region different from the first geographic region.

10. The non-transitory computer readable medium of claim 6 , further comprising:

reserving the first amount of cloud-based computing instances in the second zone.

11. A computing system for adaptive reserving of computing instances in a cloud-based computing system, comprising:

one or more memories;

at least one processor coupled to the one or more memories and configured to perform operations comprising:

determining a first simulated usage capacity of a first set of applications executed in a first zone of the cloud-based computing system at an initial time using a machine learning model;

determining a first amount of cloud-based computing instances in a second zone of the cloud-based computing system needed to maintain the first simulated usage capacity in an event of a failover of the first zone;

determining a second simulated usage capacity of a second set of applications executing in the first zone at a later time than the initial time using the machine learning model, wherein the second set of applications is different from the first set of applications;

determining a second amount of cloud-based computing instances in the second zone needed to maintain the first and second simulated usage capacities in the event of a failover of the first zone;

reserving the second amount of cloud-based computing instances in the second zone when the second amount is less than the first amount; and

dynamically modifying the reserved second amount of cloud-based computing instances in the second zone when the reserved amount does not align with current data trends continuously provided from the machine learning model.

12. The computing system of claim 11 , wherein to perform the reserving, the operations comprise generating a capacity reservation request to reserve the second amount of cloud-based computing instances in the second zone.

13. The computing system of claim 11 , wherein to perform the reserving, the operations further comprise transmitting the request to a capacity reservation service of the cloud-based computing system.

14. The computing system of claim 11 , wherein:

the first zone is distributed across a first geographic region; and

the second zone is distributed across a second geographic region different from the first geographic region.

15. The computing system of claim 11 , further comprising:

reserving the first amount of cloud-based computing instances in the second zone.

Assignments (1)

Continuity (1)

References Cited (32)