Zero-Code Kubernetes Data Management for Containerized Workloads
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Solution Overview
Problem
The challenge lies in automating the administration of data management for containerized workloads, particularly for non-IT personnel, as existing technologies struggle to provide an intuitive and efficient way to manage sophisticated data protection needs without requiring specialized technical knowledge.
Innovation Solution
The solution involves implementing zero-code administration techniques that allow DevOps personnel to manage data management applications for containerized workloads without needing to write code. This is achieved through a system that interprets high-level data protection needs and automatically configures the necessary infrastructure, including storage, compute, and networking resources, to implement the desired data management policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional data protection application administration is used, then data management can be performed, but specialized IT training is required and operation becomes complex
Solution Approach 1:
The patent introduces an intermediary layer between the user and the complex containerized workload infrastructure. This intermediary automatically translates high-level user intentions into low-level technical configurations, eliminating the need for users to possess specialized IT knowledge while still enabling sophisticated data management operations.
Solution Approach 2:
The system enables self-service data management by automatically provisioning and configuring containerized workloads based on user-defined parameters. The infrastructure self-adjusts to meet data management needs without requiring manual intervention from trained IT personnel, thereby simplifying operation while maintaining capability.
2Productivity
If automated deployment of containerized workloads is implemented, then productivity increases, but infrastructure complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-configuring containerized workload templates with all necessary infrastructure dependencies. These templates are prepared in advance with embedded configuration logic, allowing rapid deployment without requiring users to manually configure complex infrastructure components at deployment time.
Solution Approach 2:
The system creates universal containerized workload templates that can be deployed across diverse infrastructure environments. These multi-functional templates encapsulate platform-specific configurations, allowing the same template to operate on different cloud providers or on-premises infrastructure, thereby increasing productivity without proportionally increasing infrastructure complexity.
3Ease of operation
If high-level abstraction interfaces are used, then ease of operation improves, but control precision over infrastructure resources decreases
Solution Approach 1:
The patent implements feedback mechanisms that allow users to verify and adjust infrastructure resource allocations after initial automated provisioning. The system monitors actual resource usage and configuration status, providing feedback loops that enable users to refine resource allocations while maintaining the simplicity of high-level interfaces. This ensures both ease of operation and precision in resource configuration.
Data Source
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.