Kubernetes Application Grouping via Reference Path Detection

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Solution Overview

Problem

In Kubernetes environments, accurately identifying application groups is challenging due to incomplete or inaccurate resource labeling, especially with custom resources created at runtime, leading to errors in application retrieval and management, as existing solutions rely on static lists or require manual labeling by administrators.

Innovation Solution

A system comprising a reference detection module to identify resource paths, a resource classification module to assign classes, and a resource grouping module to group resources into namespaces based on types and paths, generating an application group definition automatically.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If static lists of resources are used to define applications, then application definition accuracy is improved, but adaptability to runtime-created resources deteriorates

Engineering Contradiction:

Improveapplication definition accuracyVSAvoidadaptability to runtime resources

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The system transitions from static application definitions to dynamic discovery by automatically detecting runtime-created resources through reference path analysis. The resource detection module continuously monitors Kubernetes resources and builds application group definitions based on actual runtime behavior, enabling the system to adapt to dynamically created resources while maintaining definition accuracy.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system performs self-discovery of application resources by automatically analyzing reference paths between Kubernetes resources. Instead of requiring manual configuration or static definitions, the system autonomously identifies application boundaries and resource relationships through automated detection and classification mechanisms.

Inventive Principle:

Principle #25Self-service

2Measurement precision

If manual labeling of resources is required, then application definition accuracy is improved, but administrative workload increases

Engineering Contradiction:

Improveapplication definition accuracyVSAvoidadministrative workload

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system eliminates the need for manual resource labeling by automatically detecting and classifying resources based on their reference paths. The resource detection module autonomously analyzes Kubernetes resource relationships and generates application group definitions without requiring administrator intervention or manual configuration.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system replaces manual mechanical labeling processes with automated computational detection. Instead of requiring administrators to manually tag and categorize resources, the system uses automated algorithms to detect reference paths, classify resources, and generate application definitions through computational analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Ease of operation

If Helm packages are used to identify application resources, then application discovery is simplified, but coverage for non-Helm and runtime resources is reduced

Engineering Contradiction:

Improveapplication discovery simplicityVSAvoidcoverage for all resource types

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The system provides universal application discovery capability that works across all Kubernetes resource types regardless of deployment method. The reference path detection mechanism universally analyzes any Kubernetes resource and its relationships, making it applicable to Helm-deployed applications, manually created resources, and runtime-generated resources alike, eliminating the need for Helm-specific approaches.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20230244542A1System and method for automatically identifying application groups in container deployment environments

Publication Date: 2023.08.03 DRUVA INC

AI summary

A system for automatically identifying an application group in a container deployment environment is presented. The system includes a reference detection module configured to detect and store one or more reference paths corresponding to each resource type of a plurality of resources in the container deployment environment. The system further includes a resource classification module configured to assign a resource class to each resource type of the plurality of resources. The system moreover includes a resource grouping module configured to group the plurality of resources into one or more resource groups, for each namespace, based on the corresponding resource type, resource class, and one or more reference paths. The system furthermore includes an application group definition module configured to generate an application group definition based on the one or more resource groups. A related method is also presented.