Kubernetes Resource Quota Control for Cloud Capacity Allocation
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Solution Overview
Problem
Existing Kubernetes cluster management systems face challenges in efficiently allocating and managing resource capacity, leading to suboptimal performance and potential resource overloads due to lack of clear communication and control between cluster administrators and users, resulting in manual handshakes and inefficient resource utilization.
Innovation Solution
Implementing a cluster user quota controller that tracks and requests resource quota targets using the Kubernetes resource quota API, allowing for explicit communication of resource needs between cluster users and administrators, ensuring optimal resource allocation and utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual handshakes are used between cluster administrator and cluster users to determine capacity, then resource allocation can be customized, but communication overhead and traffic increase on links between administrator node and master nodes
Solution Approach 1:
The patent introduces a resource quota controller as an intermediary component that automatically manages resource allocation between cluster users and administrators. This controller listens to resource quota events and facilitates capacity determination without requiring direct manual communication, thereby reducing traffic on administrator-master node links while maintaining customized resource allocation capabilities.
2Productivity
If cluster administrator monitors resource usage to determine desired capacity, then resource allocation can be optimized, but monitoring overhead and complexity increase
Solution Approach 1:
The resource quota controller implements self-service functionality by automatically monitoring resource quota events and capacity conditions within the Kubernetes cluster. Instead of requiring external administrator monitoring, the controller autonomously detects when resource thresholds are approached and initiates capacity determination processes, thereby optimizing resource allocation while reducing monitoring system complexity and administrator overhead.
3Measurement precision
If cluster user has control over resource allocation, then resource needs can be met accurately, but control authority conflicts with administrator's full control model
Solution Approach 1:
The patent segments control authority by separating resource allocation functions into distinct components: the resource quota controller handles user-specific resource requests and monitoring, while the cluster administrator retains overall cluster management. This segmentation allows cluster users to have precise control over their resource allocation needs without creating conflicts with administrator authority, as each entity operates within its designated control domain.
Data Source
AI summary
A method and node of a network of clusters supporting containerized workloads running in workload cluster namespaces in communication with at least one workload cluster are disclosed. In one aspect, a method implemented in a workload cluster in a network of workload clusters supporting containerized workloads running in cluster namespaces in communication with at least one workload cluster is provided. A request is received from a cluster user quota controller, the request containing a proposed set of resource limits for a cluster namespace of the cluster user. The proposed set of resource limits to be evaluated is relayed to a cluster administrator quota controller. A request is received from a cluster administrator quota controller. The request contains a first set of resource limits selected from the proposed set of resource limits to apply to a cluster namespace of the cluster user.