Load adaptation architecture framework for orchestrating and managing services in a cloud computing system

Patent metadata

Field	Value
Publication number	US-11706154-B2
Application number	US-201916459078-A
Country	US
Kind code	B2
Filing date	Jul 1, 2019
Priority date	Aug 20, 2014
Publication date	Jul 18, 2023
Grant date	Jul 18, 2023

Abstract

According to one aspect of the concepts and technologies disclosed herein, a cloud computing system can include a load adaptation architecture framework that performs operations for orchestrating and managing one or more services that may operate within at least one of layers 4 through 7 of the Open Systems Interconnection (“OSI”) communication model. The cloud computing system also can include a virtual resource layer. The virtual resource layer can include a virtual network function that provides, at least in part, a service. The cloud computing system also can include a hardware resource layer. The hardware resource layer can include a hardware resource that is controlled by a virtualization layer. The virtualization layer can cause the virtual network function to be instantiated on the hardware resource so that the virtual network function can be used to support the service.

First claim

We claim:

1. A non-transitory computer-readable storage medium comprising computer-executable instructions that, when executed by a cloud computing system, cause the cloud computing system to perform operations comprising:
detecting, in a monitored region comprising a virtual network function, that connections to a service at least meet a threshold;
responsive to detecting that connections to the service at least meet the threshold, determining that a scale-in or scale-out operation should be performed to adapt to load attributable to the connections to the service; and
triggering the scale-in or scale-out operation to adapt to the load.

2. The non-transitory computer-readable storage medium of claim 1 , wherein the operations further comprise predicting a demand for the virtual network function, and wherein the determination that the scale-in or scale-out operation should be performed to adapt to the load is based on the predicted demand for the virtual network function.

3. The non-transitory computer-readable storage medium of claim 2 , wherein the operations further comprise receiving usage data; and wherein predicting the demand for the virtual network function is based, at least in part, upon the usage data.

4. The non-transitory computer-readable storage medium of claim 2 , wherein predicting the demand for the virtual network function is based, at least in part, upon at least one of a usage trend, a scheduled event, a cyclic event, or a condition of a hardware resource utilized, at least in part, by the virtual network function.

5. The non-transitory computer-readable storage medium of claim 2 , wherein predicting the demand for the virtual network function is based, at least in part, upon a condition of a hardware resource utilized, at least in part by the virtual network function, and wherein the operations further comprise:
detecting a failure of the hardware resource or detecting a failure of connectivity associated with the hardware resource; and
in response to detecting the failure of the hardware resource or detecting the failure of connectivity associated with the hardware resource, moving the service to a new hardware resource.

6. The non-transitory computer-readable storage medium of claim 1 , wherein the load created by the connections to the service is detected based, at least in part, on a number of requests in the requests to the service received via the connections.

7. The non-transitory computer-readable storage medium of claim 1 , wherein the load created by requests to the service is determined based on monitoring throughput for the service.

8. The non-transitory computer-readable storage medium of claim 7 , wherein the throughput for the service corresponds to throughput of transactions.

9. The non-transitory computer-readable storage medium of claim 1 , wherein the determination that the scale-in or scale-out operation should be performed is based, at least in part, on performance data.

10. The non-transitory computer-readable storage medium of claim 9 , wherein the performance data includes at least data related to at least one of idle capacity or throughput.

11. The non-transitory computer-readable storage medium of claim 1 , wherein the determination that the scale-in or scale-out operation should be performed is based, at least in part, on a business rule.

12. The computer-readable storage medium of claim 1 , wherein the threshold is a threshold for shrinking or growing the service.

13. The computer-readable storage medium of claim 12 , wherein the operations further comprise setting a new threshold for shrinking or growing the service.

14. The computer-readable storage medium of claim 1 , wherein the scale-in or scale-out operation is a scale-in operation and the scale-in operation includes at least one of reducing or shrinking the number or capacity of resources associated with the virtual network function.

15. The computer-readable storage medium of claim 1 , wherein the scale-in or scale-out operation is a scale-out operation and the scale-out operation includes at least one of increasing or growing the number or capacity of resources associated with the virtual network function.

16. A computer system comprising:
one or more processors; and
a computer-readable storage medium comprising instructions that, when executed by at least one of the one or more processors, cause the computer system to:
detect, in a monitored region of a cloud computing system, that connections to a service at least meet a threshold, wherein the monitored region comprises a virtual network function;
responsive to detecting that connections to the service at least meet the threshold, determine that a scale-in or scale-out operation should be performed to adapt to load attributable to the connections to the service; and
trigger the scale-in or scale-out operation to adapt to the load.

17. The computer system of claim 16 , wherein the instructions, when executed, further cause the computer system to: predict demand for the virtual network function, and wherein the determination that the scale-in or scale-out operation should be performed to adapt to the load is based on the predicted demand for the virtual network function.

18. The computer system of claim 17 , wherein the instructions, when executed, further cause the computer system to: receive usage data; and wherein predicting the demand for the virtual network function is based, at least in part, upon the usage data.

19. The computer system of claim 17 , wherein predicting the demand for the virtual network function is based, at least in part, upon at least one of a usage trend, a scheduled event, a cyclic event, or a condition of a hardware resource utilized, at least in part, by the virtual network function.

20. The computer system of claim 17 , wherein predicting the demand for the virtual network function is based, at least in part, upon a condition of a hardware resource utilized, at least in part by the virtual network function, and wherein the instructions, when executed, further cause the computer system to:
detect a failure of the hardware resource or detecting a failure of connectivity associated with the hardware resource; and
in response to detecting the failure of the hardware resource or detecting the failure of connectivity associated with the hardware resource, move the service to a new hardware resource.

21. The computer system of claim 16 , wherein the load created by the connections to the service is detected based, at least in part, on a number of requests in the requests to the service received via the connections.

22. The computer system of claim 16 , wherein the load created by requests to the service is determined based on monitoring throughput for the service.

23. The computer system of claim 22 , wherein the throughput for the service corresponds to throughput of transactions.

24. The computer system of claim 16 , wherein the determination that the scale-in or scale-out operation should be performed is based, at least in part, on performance data.

25. The computer system of claim 24 , wherein the performance data includes at least data related to at least one of idle capacity or throughput.

26. The computer system of claim 16 , wherein the determination that the scale-in or scale-out operation should be performed is based, at least in part, on a business rule.

27. The computer system of

Assignees

• Shopify Inc

Inventors

• Parikh Anisa
• Afshar Siroos K
• Bajpay Paritosh
• Khowash Pradeep
• Klein Reuben

Classifications

• H04L47/762 Primary

  triggered by the network · CPC title

• H04L43/0811

  by checking connectivity · CPC title

• H04L43/20 Primary

  the monitoring system or the monitored elements being virtualised, abstracted or software-defined entities, e.g. SDN or NFV · CPC title

• H04L47/125

  by balancing the load, e.g. traffic engineering · CPC title

• H04L47/76

  using dynamic resource allocation, e.g. in-call renegotiation requested by the user or requested by the network in response to changing network conditions · CPC title