AUTOMATED RIGHTSIZING OF CONTAINERIZED APPLICATION WITH OPTIMIZED HORIZONTAL SCALING

1 . A method for rightsizing an application, comprising:

analyzing, via processing circuitry, metrics from containers in the application;

determining, via the processing circuitry, a resource allocation and a target resource utilization for an application workload based on the metrics; and

configuring, via the processing circuitry, the application workload based on the resource allocation, the target resource utilization, and the metrics.

2 . The method of claim 1 , wherein the metrics are collected from deployed containers and analyzed using machine learning to determine the resource allocation and the target resource utilization for the application workload.

3 . The method of claim 2 , further comprising receiving a configuration input from a user, wherein the machine learning is configured based on the configuration input.

4 . The method of claim 1 , wherein the resource allocation and the target resource utilization are determined in tandem and wherein the determination of the target resource utilization is used for simultaneous horizontal scaling of the application workload.

5 . The method of claim 1 , further comprising generating, using machine learning, a prediction of resource usage metrics and operating costs based on the determined resource allocation and the determined target resource utilization before configuring the application workload.

6 . The method of claim 1 , further comprising continuously and automatically configuring the application workload with the determined resource allocation and the determined target resource utilization.

7 . A device comprising:

processing circuitry configured to:

analyze metrics from containers in an application,

determine a resource allocation and a target resource utilization for an application workload based on the metrics, and

configure the application workload based on the resource allocation, the target resource utilization, and the metrics.

8 . The device of claim 7 , wherein the metrics are collected from deployed containers and analyzed using machine learning to determine the resource allocation and the target resource utilization for the application workload.

9 . The device of claim 8 , wherein the processing circuitry is further configured to receive a configuration input from a user, wherein machine learning is configured based on the configuration input.

10 . The device of claim 7 , wherein the resource allocation and the target resource utilization are determined in tandem and wherein the determination of the target resource utilization is used for simultaneous horizontal scaling of the application workload.

11 . The device of claim 7 , wherein the processing circuitry is further configured to generate, using machine learning, a prediction of resource usage metrics and operating costs based on the resource allocation and the target resource utilization before configuring the application.

12 . The device of claim 7 , wherein the processing circuitry is further configured to continuously and automatically configure the application workload with the determined resource allocation and the determined target resource utilization.

13 . A non-transitory computer-readable storage medium for storing computer-readable instructions that, when executed by a computer, cause the computer to perform a method, the method comprising:

analyzing metrics from containers in an application;

determining a resource allocation and a target resource utilization for an application workload based on the metrics; and

configuring the application workload based on the resource allocation, the target resource utilization, and the metrics.

14 . The non-transitory computer-readable storage medium of claim 13 , wherein the metrics are collected from deployed containers and analyzed using machine learning to determine the resource allocation and the target resource utilization for the application workload.

15 . The non-transitory computer-readable storage medium of claim 14 , further comprising receiving a configuration input from a user, wherein the machine learning is configured based on the configuration input.

16 . The non-transitory computer-readable storage medium of claim 13 , wherein the resource allocation and the target resource utilization are determined in tandem and wherein the determination of the target resource utilization is used for simultaneous horizontal scaling of the application workload.

17 . The non-transitory computer-readable storage medium of claim 13 , further comprising generating, using machine learning, a prediction of resource usage metrics and operating costs based on the determined resource allocation and the determined target resource utilization before configuring the application workload.

18 . The non-transitory computer-readable storage medium of claim 13 , further comprising continuously and automatically configuring the application workload with the determined resource allocation and the determined target resource utilization.