Declarative Kubernetes Cluster Administration for Data Management
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Solution Overview
Problem
The challenge lies in managing sophisticated data management applications for containerized workloads, particularly in Kubernetes clusters, where non-IT personnel face the burden of manual administration due to the increased scope and complexity, necessitating automated solutions for zero-code formation and ongoing administration.
Innovation Solution
The implementation of a system that automates the formation and ongoing administration of Kubernetes clusters through a sequence of instructions stored on a non-transitory computer readable medium, utilizing agents to manage resources and policies, and providing a user-friendly interface for non-IT personnel to express data protection needs, which are then translated into actionable commands for infrastructure management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If non-IT personnel manually administer data management applications for containerized workloads, then they can directly control and configure systems, but the complexity and burden of administration increases significantly
Solution Approach 1:
The patent introduces an intermediary automation layer between non-IT personnel and the complex Kubernetes infrastructure. This automation layer translates high-level user intentions into low-level technical configurations, allowing non-IT personnel to administer data management applications without directly dealing with the underlying complexity of container orchestration, persistent volumes, and storage configurations.
Solution Approach 2:
The system enables self-service automation where the administration platform automatically performs complex configuration tasks without requiring manual intervention from administrators. The platform autonomously handles resource allocation, policy enforcement, and system configuration based on user-defined parameters, eliminating the need for non-IT personnel to manually navigate complex technical settings.
2Adaptability or versatility
If sophisticated data management applications are deployed in Kubernetes clusters, then functional capabilities and features are enhanced, but the burden on administrators increases
Solution Approach 1:
The patent segments the administration process into distinct functional modules, each handling specific aspects of data management such as backup, restore, replication, and policy enforcement. This modular segmentation allows sophisticated applications to be composed of independent, manageable components that can be configured and administered separately, reducing the overall administration burden despite increased functional sophistication.
Solution Approach 2:
The automation layer acts as an intermediary that manages the complexity of sophisticated data management applications. It provides a simplified interface that abstracts away the intricate configurations required for Kubernetes persistent volumes, storage classes, and data protection policies, allowing administrators to leverage advanced features without being overwhelmed by their complexity.
3Extent of automation
If manual configuration methods are used for Kubernetes clusters, then flexibility and control are maintained, but automation and efficiency are reduced
Solution Approach 1:
The system implements self-service automation where the platform automatically generates and applies Kubernetes configurations based on user-defined parameters. The automation engine autonomously creates persistent volume claims, storage classes, and data management policies without requiring manual configuration, thereby achieving high formation automation while managing configuration complexity through intelligent automation.
Solution Approach 2:
The patent replaces manual mechanical configuration processes with automated software-based systems. Instead of administrators manually configuring Kubernetes resources through complex CLI commands or UI interfaces, the system uses automated agents and orchestration logic to programmatically establish and manage cluster configurations, transitioning from manual mechanical operations to automated software control.
Data Source
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AI summary
Methods, systems, and computer program products for self-service, zero-code administration of data management activities arising from containerized workloads. A Kubernetes cluster deployment module is configured to synthesize operations that are intended to achieve a desired state of data management functions. The intention is provided by a self-service user using declarative, no-code specifications. Based on the declarative, no-code specifications, a cluster creation module implements operations to create or configure a Kubernetes cluster. The configured Kubernetes cluster is configured with sufficient resources to be able to initially implement the desired state of the data management functions and to maintain the desired state of the data management functions under changing conditions. As applicable, the storage infrastructure on which the Kubernetes cluster is implemented is continually instructed to maintain the desired state of the data management functions. The storage infrastructure can be implemented as an HCI cluster or as some other storage infrastructure.