Automated Kubernetes adaptation through a digital twin
AssigneesRakuten Symphony, Inc., Technical University of Munich
InventorsJohannes Peter Donato Zerwas, Patrick Michael Krämer, Wolfgang Leonhard Kellerer, Navidreza Asadi, Razvan-Mihai Ursu, Philip Rodgers, Jee Chang Leon Wong
Life of this patent
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Abstract
A method of workload management in a Kubernetes (K8s) environment may include obtaining, by a digital twin (DT) representing a cluster state, performance data of at least one K8s cluster, generating, by the DT, a behavioral model based on the performance data, determining, by a horizontal pod autoscaler (HPA) controller, a HPA configuration based on the behavioral model and implementing, by an HPA of the at least one K8s cluster, the determined HPA configuration.
Claims20
A method of workload management in a Kubernetes (K8s) environment, comprising:
- obtaining, by a digital twin (DT) representing a cluster state, performance data of at least one K8s cluster;
- generating, by the DT, a behavioral model based on the performance data;
- determining, by a horizontal pod autoscaler (HPA) controller, a HPA configuration based on the behavioral model;
- and
- implementing, by an HPA of the at least one K8s cluster, the determined HPA configuration;
- wherein the digital twin comprises a measurements database configured to store the performance data obtained from a production cluster and a canary cluster, and
- wherein the performance data comprises at least one of resource utilization information from a K8s cAdvisor, HPA actions of an HPA of the production cluster, and a deployment time of at least one pod of the production cluster.
A system for workload management in a Kubernetes (K8s) environment, comprising:
- at least one memory storing instructions;
- and
- at least one processor configured to execute the instructions to:
- obtain, by a digital twin (DT) representing a cluster state, performance data of at least one K8s cluster;
- generate, by a simulation generator, at least one horizontal pod autoscaler (HPA) configuration based on a behavioral model;
- determine, by a HPA controller, a HPA configuration based on the behavioral model;
- and
- implement, by an HPA of the at least one K8s cluster, the determined HPA configuration,
- wherein the digital twin comprises a measurements database configured to store the performance data obtained from a production cluster and a canary cluster, and
- wherein the performance data comprises at least one of resource utilization information from a K8s cAdvisor, HPA actions of an HPA of the production cluster, and a deployment time of at least one pod of the production cluster.
A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to:
- obtain, by a digital twin (DT) representing a cluster state, performance data of at least one Kubernetes (K8s) cluster;
- generate, by the DT, a behavioral model based on the performance data;
- determine, by a horizontal pod autoscaler (HPA) controller, a HPA configuration based on the behavioral model;
- and
- implement, by an HPA of the at least one K8s cluster, the determined HPA configuration,
- wherein the digital twin comprises a measurements database configured to store the performance data obtained from a production cluster and a canary cluster, and
- wherein the performance data comprises at least one of resource utilization information from a K8s cAdvisor, HPA actions of an HPA of the production cluster, and a deployment time of at least one pod of the production cluster.
Dependencies are inferred from the claim text.