File Prioritization via Euclidean Distance for Data Restore
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Solution Overview
Problem
Conventional data protection systems lack a mechanism to prioritize incoming data files during multi-object restores, leading to potential data loss in disaster scenarios where critical files are queued behind non-critical files, compromising the recovery point objective (RPO).
Innovation Solution
A file-level prioritization method that utilizes file-type information and Euclidean distance calculations to determine the criticality of files, ensuring that critical data is restored first by assigning weighted values and sorting files based on their calculated distances, thereby prioritizing the most critical files for immediate attention during restore operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If files are restored using first come first serve protocol, then restore operations can proceed in parallel, but critical files may be queued behind non-critical files leading to data loss
Solution Approach 1:
The system performs preliminary classification of files into critical and non-critical categories before the restore operation begins. Critical files are identified using multiple criteria including file type analysis, filename pattern matching, and user-defined priorities. This preliminary action ensures that when the restore operation starts, the system already knows which files must be restored first, eliminating the risk of critical files being queued behind non-critical ones while maintaining parallel restore operations for optimal speed.
2Ease of operation
If all backups are treated as normal backups without priority differentiation, then the system is simpler to operate, but RPO cannot be achieved for critical clients during disasters
Solution Approach 1:
The system segments the backup operation into distinct priority levels (P0, P1, P2, P3) corresponding to critical, high, medium, and low priority files. Each segment is handled with appropriate restore precedence. The segmentation is implemented through automated file classification that assigns priority levels based on file characteristics and user-defined rules, allowing the system to maintain simplicity of operation while achieving differentiated restore behavior to meet RPO requirements for critical clients.
3Loss of time
If restore operations run in parallel for all selected data sets, then overall restore time is reduced, but critical data may not be restored first during disaster scenarios
Solution Approach 1:
The system applies local quality by assigning different restore priorities to different files based on their criticality. While multiple restore operations run in parallel, each file restore operation is tagged with a priority level that determines its scheduling precedence. Critical files (P0) are restored with highest priority, ensuring they complete first even though parallel operations are occurring. This local differentiation of quality (priority) within the parallel operation framework ensures both time efficiency and critical data availability.
Data Source
AI summary
Methods and systems for file level prioritization during a data restore operation are disclosed. According to some embodiments, in response to a restore request to restore one or more files and for each file, the method includes retrieving file information and a critical value associated with the file, and calculating a Euclidean distance of the file to a consecutive file based on the critical value. The method further includes sorting the file(s) based on the calculated Euclidean distance of each file. The method further includes restoring the sorted file(s) at a target site.