Metadata-Driven Asset Grouping for Automated Backup Policy Assignment
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Solution Overview
Problem
Current data backup systems face challenges in efficiently assigning protection policies to new data assets, as manual assignment is time-consuming and prone to errors, while rule-based approaches require significant administrative effort and can lead to simplistic or erroneous rules.
Innovation Solution
A data asset protection system that leverages asset metadata to automatically assign policies by identifying common characteristics of assets, using a metadata-driven process to cluster assets and calculate affinity scores for optimal policy matching, thereby reducing administrative burden and improving policy accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual assignment of backup policies to data assets is used, then administrators can apply appropriate protection policies to specific data types, but it is time consuming and newly added assets do not receive immediate protection
Solution Approach 1:
The system enables new data assets to automatically self-assign to backup policies by evaluating their own metadata characteristics against policy criteria. The policy assignment module continuously monitors for new assets and automatically matches them with appropriate policies based on metadata similarity, eliminating the need for manual administrator intervention and ensuring immediate protection coverage.
2Productivity
If rule-based policy assignment is used, then new assets can be automatically added to policies, but creation of rules by administrators is time consuming and may result in rules that are too simplistic and prone to errors
Solution Approach 1:
The system replaces the mechanical process of manual rule creation with an automated metadata-driven assignment mechanism. Instead of administrators manually crafting complex rules, the system uses the policy assignment module to automatically analyze asset metadata, identify patterns, and generate appropriate policy assignments based on similarity comparisons, thereby reducing both administrative burden and rule complexity.
Solution Approach 2:
The system dynamically adjusts policy assignment parameters by continuously analyzing metadata characteristics of assets and policies. Rather than using fixed, static rules, the system modifies assignment criteria based on observed metadata patterns and asset similarities, enabling more accurate and adaptive policy assignments without requiring complex predetermined rules.
3Measurement precision
If manual assignment of backup policies is performed, then administrators can ensure accurate policy application, but it requires significant administrative effort and does not scale well
Solution Approach 1:
The system enables automatic self-assignment of backup policies to data assets by continuously monitoring for new assets and automatically matching them with appropriate policies based on metadata similarity. This eliminates the need for manual administrator intervention while maintaining accurate policy application through automated comparison of asset characteristics against policy criteria.
Solution Approach 2:
The system implements a feedback mechanism where the policy assignment module continuously monitors asset metadata, compares it with existing policy criteria, and automatically adjusts assignments based on the similarity analysis. This closed-loop approach ensures accurate policy application while reducing administrative effort, as the system learns from and adapts to metadata patterns over time.
Data Source
AI summary
Embodiments for a data protection method of grouping assets for protection policy assignment based on asset metadata by defining a set of metrics characterizing each asset in the system and comparing each metric of an asset with corresponding metrics of other asset groups each containing one or more other assets. A unique protection policy is assigned to each group for application to each asset within a respective group. An overall affinity percentage of the metrics of asset with the corresponding metrics of each group is determined, and the asset is automatically grouped into the group based with the highest overall affinity percentage. The user is prompted to confirm the automatic grouping or to select a different group for assigning to the asset.