Kubernetes Policy Controller for Unified Resource Management
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Solution Overview
Problem
Current container orchestration systems, such as Kubernetes, require the development of scenario-specific controllers for granular resource management, which is time- and resource-intensive and leads to higher consumption of technical resources, as conditions for resource changes need to be evaluated during runtime.
Innovation Solution
A policy custom resource is introduced that enables the definition and enforcement of continuously evaluated dynamic policies on resources within a container orchestration system, allowing for the application of policies to multiple resources without the need for separate scenario-specific controllers, using a policy target, condition, and action framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If scenario-specific controllers are developed for granular resource management, then resource management flexibility is improved, but device complexity and resource consumption increase
Solution Approach 1:
The patent introduces a universal policy controller that can manage multiple resource types through a single unified architecture. Instead of creating separate controllers for each resource scenario, the policy controller uses a common evaluation framework that processes policies for different resources (pods, nodes, services) through the same mechanism, thereby reducing complexity while maintaining flexibility.
Solution Approach 2:
The patent introduces policy custom resources as intermediaries between the desired state and actual resource state. These policy custom resources encapsulate the management logic for different scenarios, allowing the policy controller to delegate specific management tasks without needing to understand the underlying complexity of each resource type. The policy custom resources act as mediators that translate high-level policies into resource-specific actions.
2Manufacturing precision
If scenario-specific controllers are developed for runtime condition evaluation, then management precision is improved, but loss of time and resources increase
Solution Approach 1:
The patent allows operators to define policies in advance using policy custom resources that specify desired states and evaluation conditions. These policies are prepared and stored before actual resource management operations are needed. When resources are created or modified, the policy controller automatically retrieves and applies the relevant pre-defined policies, eliminating the need for ad-hoc controller development for each scenario.
Solution Approach 2:
The patent enables dynamic adjustment of policy parameters without changing the underlying controller architecture. Operators can modify policy conditions, target resource types, and action parameters through the policy custom resource mechanism, allowing the system to adapt to different management scenarios by changing parameters rather than rewriting controllers.
3Adaptability or versatility
If multiple scenario-specific controllers are implemented, then adaptability is improved, but use of energy increases
Solution Approach 1:
The patent merges the functionality of multiple scenario-specific controllers into a single policy controller. Instead of having separate controllers for different resource types and scenarios, the policy controller consolidates all policy evaluation and enforcement operations into one unified process, reducing the total number of active controllers and their associated resource consumption while maintaining the ability to handle diverse management scenarios.
Solution Approach 2:
The policy controller is designed as a universal system that can handle multiple resource types and policy scenarios through a single architecture. It uses a common evaluation framework that processes policies for pods, nodes, services, and other resources through the same mechanism, thereby reducing the total number of controllers needed and lowering overall system resource consumption.
Data Source
AI summary
Methods, systems, and computer-readable storage media for executing, by a policy controller, a policy custom resource that defines a policy to be applied to target resources within a cluster of a cloud computing platform, the policy custom resource including a policy target defining parameters for identifying a resource as a target resource, a policy condition defining conditions of a target resource that are to be evaluated, and a policy action defining actions that are to be executed in response to the conditions evaluating to true, receiving, by the policy controller, a set of target resources based on the policy target of the policy custom resource, evaluating, by the policy controller and for each target resource in the set of target resources, the policy condition, and in response to the policy condition returning as true for a target resource, executing the policy action for the target resource.