Customized coordinated scaling of cloud-based applications and backend resources

Patent metadata

Field	Value
Publication number	US-10503553-B1
Application number	US-201916273994-A
Country	US
Kind code	B1
Filing date	Feb 12, 2019
Priority date	Feb 12, 2019
Publication date	Dec 10, 2019
Grant date	Dec 10, 2019

Abstract

Methods and apparatuses are described for coordinated scaling of a cloud-based software application and a backend resource in a networked computing environment. A server captures resource usage metrics associated with cloud-based applications. The server captures resource usage metrics associated with backend resources that correspond to the cloud-based applications. The server aggregates the resource usage metrics to generate a set of integrated application-level resource usage metrics. The server determines a current resource consumption level based upon the integrated application-level resource usage metrics for the cloud-based software application. The server compares the current resource consumption level to a desired resource consumption level. The server computing device changes resource availability for the cloud-based software application, based upon a difference between the current resource consumption level and the desired resource consumption level.

First claim

What is claimed is:

1. A system for coordinated scaling of a cloud-based software application and a backend resource in a networked computing environment, the system comprising a server computing device having a memory that stores computer-executable instructions and a processor that executes the computer-executable instructions to:
capture usage metrics associated with one or more cloud-based software applications, each cloud-based software application comprising one or more application containers;
capture usage metrics associated with one or more backend resources being used by the one or more cloud-based software applications;
aggregate the usage metrics associated with the one or more cloud-based software applications and the usage metrics associated with the one or more backend resources to generate a set of integrated application-level usage metrics, comprising:
identifying, for each cloud-based software application, one or more resources of the backend resources being used by the cloud-based software application, based upon metadata associated with the cloud-based software application, and
matching, for each cloud-based software application, the usage metrics associated with the identified resources of the backend resources to the usage metrics for the cloud-based software application;

determine, for each of the one or more cloud-based software applications, a current resource consumption level based upon the integrated application-level resource usage metrics for the cloud-based software application;
compare, for each of the one or more cloud-based software applications, the current resource consumption level to a desired resource consumption level for the cloud-based software application; and
change, for each of the one or more cloud-based software applications, one or more of: a CPU resource availability for the cloud-based software application, a memory resource availability for the cloud-based software application, a number of application containers in the cloud-based software application, a latency score for the cloud-based software application, or a priority level for the cloud-based software application, based upon a difference between the current resource consumption level and the desired resource consumption level.

2. The system of claim 1 , wherein the usage metrics associated with one or more cloud-based software applications comprise CPU usage data, memory usage data, network latency data, and a number of active application containers.

3. The system of claim 1 , wherein the usage metrics associated with one or more backend resources comprise connection count data, connection pool data, CPU usage data, memory usage data, and peak consumption cost data.

4. The system of claim 1 , wherein the desired resource consumption level is based upon one or more resource consumption policies stored in a database coupled to the server computing device.

5. The system of claim 4 , wherein one or more of the resource consumption policies are specific to a cloud-based software application.

6. The system of claim 1 , wherein the server computing device identifies one or more dependencies for each cloud-based software application, the one or more dependencies comprising a second cloud-based software application that provides data to the cloud-based software application.

7. The system of claim 6 , wherein the server computing device changes one or more of: a CPU resource availability for the second cloud-based software application, a memory resource availability for the second cloud-based software application, a number of application containers in the second cloud-based software application, or a priority level for the second cloud-based software application, based upon the difference between the current resource consumption level and the desired resource consumption level.

8. The system of claim 1 , wherein the priority level of the cloud-based software application comprises a flag indicating a priority at which the cloud-based software application should have access to computing resources.

9. The system of claim 1 , wherein the latency score of the cloud-based software application comprises a network latency tolerance value for the cloud-based software application.

10. The system of claim 1 , wherein the one or more backend resources comprises mainframe databases, cloud databases, and/or web services.

11. The system of claim 1 , wherein the CPU resource availability for the cloud-based software application comprises an amount of CPU processing bandwidth usable by the cloud-based software application.

12. The system of claim 11 , wherein the CPU resource availability for the cloud-based software application comprises an amount of CPU processing bandwidth usable by each application container in the cloud-based software application.

13. The system of claim 1 , wherein the memory resource availability for the cloud-based software application comprises an amount of memory space usable by the cloud-based software application.

14. The system of claim 13 , wherein the memory resource availability for the cloud-based software application comprises an amount of memory space usable by each application container in the cloud-based software application.

15. A computerized method of coordinated scaling of a cloud-based software application and a backend resource in a networked computing environment, the method comprising:
capturing, by a server computing device, usage metrics associated with one or more cloud-based software applications, each cloud-based software application comprising one or more application containers;
capturing, by the server computing device, usage metrics associated with one or more backend resources that being used by the one or more cloud-based software applications;
aggregating, by the server computing device, the usage metrics associated with the one or more cloud-based software applications and the usage metrics associated with the one or more backend resources to generate a set of integrated application-level usage metrics, comprising:
identifying, for each cloud-based software application, one or more resources of the backend resources being used by the cloud-based software application, based upon metadata associated with the cloud-based software application, and
matching, for each cloud-based software application, the usage data associated with the identified resources of the backend resources to the usage data for the cloud-based software application;

determining, by the server computing device for each of the one or more cloud-based software applications, a current resource consumption level based upon the integrated application-level usage metrics for the cloud-based software application;
comparing, by the server computing device for each of the one or more cloud-based software applications, the current resource consumption level to a desired resource consumption level for the cloud-based software application; and
changing, by the server computing device for each of the one or more cloud-based software applications, one or more of: a CPU resource availability for the cloud-based software application, a memory resource availability for the cloud-based software application, a number of application containers in the cloud-based software application, a latency score for the cloud-based software application, or a priority level for the cloud-based software application, based upon a difference between the current resource consumption level and the desired resource consumption level.

16. The method of claim 15 , wherein the usage metrics associated with one or more cloud-based s

Assignees

• Fmr Llc

Inventors

• Ashok Rohith
• Eriksen Stein

Classifications

• G06F9/5011 Primary

  the resources being hardware resources other than CPUs, Servers and Terminals · CPC title

• G06F9/5083

  Techniques for rebalancing the load in a distributed system · CPC title

• G06F9/5077

  Logical partitioning of resources; Management or configuration of virtualized resources (specific details on emulation or internal functioning of virtual machines G06F9/455) · CPC title

• G06F11/3433

  for load management (allocation of a server based on load conditions G06F9/505; load rebalancing G06F9/5083; redistributing the load in a network by a load balancer H04L67/1029) · CPC title

• G06F11/3024 Primary

  where the computing system component is a central processing unit [CPU] · CPC title