Automated rightsizing of containerized application with optimized horizontal scaling
AssigneeCloudbolt Software Inc.
InventorsBradley Joseph Beam, Jeremy Michael Gustie, Christopher Marc Larson, Thibaut Xavier Perol, John Daniel Platt
Life of this patent
Estimating the term…
Abstract
A method for rightsizing an application, including analyzing, via processing circuitry, metrics from containers; 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.
Claims19
A method for rightsizing an application, comprising:
- acquiring, via processing circuitry, one or more metrics from containers in the application;
- determining both a resource allocation and a target resource utilization corresponding to an application workload of the application by inputting the acquired one or more metrics to a machine-learning model, the machine-learning model being configured to simultaneously output both (1) a recommended resource allocation indicating a number of allocated resources, and (2) a recommended target resource utilization indicating a target percentage usage of each allocated resource, the recommended resource allocation and the recommended target resource utilization being determined in tandem by the machine-learning model;
- and
- automatically configuring, via the processing circuitry, the application workload based on the determined resource allocation, the determined target resource utilization, and the acquired one or more metrics.
A device, comprising:
- processing circuitry configured to:
- acquire one or more metrics from containers in an application,
- determine both a resource allocation and a target resource utilization corresponding to an application workload of the application by inputting the acquired one or more metrics to a machine-learning model, the machine-learning model being configured to simultaneously output both (1) a recommended resource allocation indicating a number of allocated resources, and (2) a recommended target resource utilization indicating a target percentage usage of each allocated resource, the recommended resource allocation and the recommended target resource utilization being determined in tandem by the machine-learning model, and
- automatically configure the application workload based on the determined resource allocation, the determined target resource utilization, and the acquired one or more metrics.
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:
- acquiring one or more metrics from containers in an application;
- determining both a resource allocation and a target resource utilization corresponding to an application workload of the application by inputting the acquired one or more metrics to a machine-learning model, the machine-learning model being configured to simultaneously output both (1) a recommended resource allocation indicating a number of allocated resources, and (2) a recommended target resource utilization indicating a target percentage usage of each allocated resource, the recommended resource allocation and the recommended target resource utilization being determined in tandem by the machine-learning model;
- and
- automatically configuring the application workload based on the determined resource allocation, the determined target resource utilization, and the acquired one or more metrics.
Dependencies are inferred from the claim text.