Computing Object Recovery Blueprint Suggestions Through Grouping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manually generating blueprints for recovering large quantities of computing objects is a burdensome task, especially for customers lacking real-time data and information, making it unfeasible and inefficient.
Innovation Solution
A data management system analyzes computing object characteristics to generate suggested blueprints for recovery, reducing the burden on customers by providing selectable options tailored to their needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If blueprints for recovering computing objects are manually generated, then customization and control are improved, but the burden and time consumption increase significantly
Solution Approach 1:
The system performs preliminary analysis of computing object characteristics, dependencies, and recovery requirements before blueprint generation. By pre-processing and organizing this information, the system reduces the time and effort needed for manual blueprint creation while maintaining high customization capability.
Solution Approach 2:
The system enables automated blueprint generation that serves itself by analyzing its own managed computing objects and automatically creating recovery blueprints based on detected characteristics and relationships, reducing reliance on manual intervention.
2Reliability
If blueprints are generated for large quantities of computing objects, then comprehensive recovery coverage is improved, but the complexity and burden of generation increase
Solution Approach 1:
The system segments the large quantity of computing objects into groups based on characteristics, dependencies, and recovery requirements. This segmentation allows the system to manage complexity by handling objects in manageable groups while ensuring comprehensive recovery coverage across all segments.
Solution Approach 2:
The system changes parameters such as grouping criteria, recovery priorities, and resource allocation based on the analyzed characteristics of computing objects. This dynamic parameter adjustment enables the system to generate appropriate blueprints for large quantities of objects without overwhelming complexity.
3Ease of operation
If customers generate blueprints without real-time data and information, then manual control is maintained, but the feasibility and efficiency decrease
Solution Approach 1:
The system implements feedback mechanisms that collect real-time data and information about computing objects, their status, and relationships. This feedback is used to inform and improve blueprint generation, ensuring that blueprints are based on current, accurate information while maintaining customer control over the process.
Data Source
AI summary
Techniques for data management at a data management system are described. Characteristics for a set of computing objects managed by a computing system may be determined. The characteristics determined for the set of computing objects may be used to group the computing objects into groups. Based on grouping the computing objects, instructions for recovering the computing objects may be generated. The instructions may be provided to a user as a suggestion. Based on receiving a selection of the suggested instructions, the suggested instructions may be configured for a recovery of the computing objects of the computing system.