Control Plane Graph for Storage Configuration Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual procedures for provisioning and scaling data storage in cloud computing are time-consuming and costly, as they require ensuring valid configuration information and requirements for computing resources, which can lead to errors and inefficiencies.
Innovation Solution
A proposal-based control plane system using a graph data structure to model computing resources and apply configuration changes, with APIs to request updates and validate against configuration constraints, ensuring reliable and efficient configuration management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual procedures are used for provisioning and scaling data storage, then configuration flexibility is maintained, but time consumption and error rates increase
Solution Approach 1:
The system performs preliminary validation of configuration parameters against predefined constraints before applying configuration changes. The control plane validates proposed configurations against storage system constraints, ensuring correctness before execution, which eliminates the need for time-consuming manual verification while maintaining flexibility.
Solution Approach 2:
The system provides automated feedback through the control plane that validates configuration proposals against stored constraints and returns validation results to the user. This feedback mechanism automatically checks configuration correctness, reducing time consumption while preserving the ability to make flexible configuration changes.
2Ease of operation
If manual procedures are used for provisioning and scaling data storage, then configuration control is maintained, but error rates and costs increase
Solution Approach 1:
The control plane provides automated feedback by validating configuration proposals against predefined constraints before changes are applied. This feedback loop automatically detects configuration errors, preventing them from propagating to the data plane, thus maintaining configuration control while significantly reducing error rates.
Solution Approach 2:
The system applies preliminary anti-action by proactively validating configurations against constraints before they can cause errors. The control plane checks proposed configurations against stored constraints and prevents invalid configurations from being applied, thereby maintaining control while reducing errors and associated costs.
3Adaptability or versatility
If configuration validation is performed manually, then flexibility in configuration changes is maintained, but productivity decreases
Solution Approach 1:
The control plane performs preliminary validation of configuration proposals against predefined constraints before changes are applied to the data plane. This automated preliminary check maintains flexibility in configuration changes while dramatically improving productivity by eliminating manual validation steps.
Solution Approach 2:
The system provides self-service configuration validation where the control plane automatically checks proposed configurations against stored constraints without requiring manual intervention. This self-service mechanism preserves configuration flexibility while enhancing productivity through automated validation.
Data Source
AI summary
Disclosed techniques generate a data structure including data representing a configuration of a data storage system. A parameter can be obtained that is usable to configure the data storage system. Data representing the configuration of the data storage system can be modified based on the parameter. The data storage system can be configured, using the parameter, based on the modified data representing the configuration of the data storage system and predetermined configuration data for the data storage system.