Recovery Blueprints for Grouped Computing Objects
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Solution Overview
Problem
Manually generating blueprints for recovering large quantities of computing objects is burdensome and often unfeasible for customers, especially when real-time data for efficient recovery is not available.
Innovation Solution
A data management system analyzes computing object characteristics and generates suggested blueprints for recovery, reducing the burden on customers by providing automated and efficient recovery strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If customers manually generate blueprints for recovering computing objects, then recovery plans can be customized according to specific needs, but the manual effort and time required increases significantly especially when real-time data is not available
Solution Approach 1:
The system enables self-service by automatically generating recovery blueprints using real-time data from computing objects without requiring manual customer input. The data management system autonomously collects object characteristics, determines recovery priorities, and creates optimized blueprints, eliminating the time-consuming manual process while maintaining high accuracy through algorithmic analysis of actual system state
Solution Approach 2:
The system performs preliminary action by continuously collecting and analyzing real-time data about computing objects before recovery events occur. This advance preparation includes monitoring object characteristics, relationships, and operational status, so that when recovery is needed, blueprints are already generated and ready based on current system state rather than requiring post-event manual assessment
2Reliability
If customers manually generate blueprints for large quantities of computing objects, then comprehensive recovery coverage can be achieved, but the complexity and feasibility of the task becomes unmanageable
Solution Approach 1:
The system replaces the mechanical manual process of blueprint generation with automated computational systems. Algorithms analyze computing object characteristics, determine interrelationships, calculate recovery priorities, and generate comprehensive blueprints automatically. This substitution handles large quantities of objects systematically through code-based logic rather than human manual processes, achieving complete coverage without overwhelming complexity
Solution Approach 2:
The system applies segmentation by dividing the complex task of generating blueprints for large quantities of computing objects into manageable components: collecting object characteristics, analyzing relationships, determining priorities, and generating recovery sequences. This modular approach processes objects in organized groups rather than overwhelming manual review, maintaining comprehensiveness while reducing operational complexity
3Productivity
If real-time data is used to generate recovery blueprints, then recovery efficiency is improved, but the need for continuous data collection and analysis increases system operational complexity
Solution Approach 1:
The system implements continuous data collection and analysis as an ongoing background process rather than a discrete task. Real-time monitoring of computing objects continuously updates object characteristics and relationships, maintaining current information without requiring separate data gathering phases. This continuous operation enables immediate blueprint generation when needed while distributing the data management workload over time, improving recovery speed without concentrating complexity at critical moments
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.