Container Workload Deployment via Hypervisor Control Plane Automation
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Solution Overview
Problem
Existing container orchestration platforms, such as Kubernetes, automate the deployment and scaling of containerized workloads but require manual intervention and operational effort for deploying and managing the control plane and worker nodes, which can lead to inefficiencies and errors.
Innovation Solution
A system that automates the deployment of containerized workloads by booting a device with a hypervisor, obtaining configuration files from an external server, and using an infravisor layer to instantiate a control plane and worker nodes based on defined manifests, allowing for logical separation and management of states without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention is used for deploying control plane and worker nodes, then operational flexibility and adaptability are maintained, but deployment efficiency and reliability deteriorate due to human errors and inefficiencies
Solution Approach 1:
The system enables self-service automation where the control plane automatically provisions worker nodes and manages workloads without requiring manual human intervention. The automated control plane reads configuration manifests, validates them against schemas, and executes deployment operations autonomously, transforming a manual process into a self-service system that improves deployment efficiency and reliability.
Solution Approach 2:
The system performs preliminary actions by pre-defining configuration manifests that specify the desired state of worker nodes and workloads before deployment. These manifests are validated against schemas in advance, ensuring correctness before execution. This preliminary configuration and validation phase enables reliable automated deployment without manual intervention during the actual deployment process.
2Reliability
If automated deployment is implemented, then deployment reliability and consistency are improved, but system complexity increases due to configuration management requirements
Solution Approach 1:
The system segments configuration management into distinct components: configuration manifests define the desired state, schemas validate the configuration structure, and the control plane executes deployments. This segmentation separates concerns and makes each component simpler and more manageable, reducing overall system complexity while maintaining high deployment reliability through automated validation and execution.
Solution Approach 2:
The system introduces schemas as intermediaries between configuration manifests and the deployment process. Schemas act as mediators that validate and interpret configuration data, ensuring consistency and correctness without requiring complex manual configuration management. This intermediary layer simplifies the relationship between configuration files and deployment operations, maintaining reliability while reducing complexity.
3Ease of operation
If control plane and worker nodes are manually managed, then operational flexibility is maintained, but operational effort and time consumption increase
Solution Approach 1:
The control plane performs self-service operations by automatically provisioning worker nodes and managing workloads based on configuration manifests. This eliminates the need for manual operational intervention, significantly reducing operational effort and time consumption. The system manages itself autonomously, transforming complex manual operations into automated self-service processes that maintain ease of operation while minimizing human involvement.
4Reliability
If infrastructure and workloads are deployed without automation, then manual control and adaptability are maintained, but error rates and inefficiencies increase
Solution Approach 1:
The system performs preliminary validation of configuration manifests against schemas before deployment execution. This preliminary action ensures that configurations are correct and consistent, preventing errors during deployment. By validating configurations in advance and executing them automatically, the system achieves error-free deployments while maintaining high automation levels, eliminating the trade-off between manual control and reliability.
Data Source
AI summary
An example method of automatically deploying a containerized workload on a hypervisor based device is provided. The method generally includes booting the device running a hypervisor, in response to booting the device: automatically obtaining, by the device, one or more intended state configuration files from a server external to the device, the one or more intended state configuration files defining a control plane configuration for providing services for at least deploying and managing the containerized workload and workload configuration parameters for the containerized workload; deploying a control plane pod configured according to the control plane configuration; deploying one or more worker nodes based on the control plane configuration, and deploying one or more workloads identified by the workload configuration parameters on the one or more worker nodes.