Kernel File System Filter for Backup Block Tracking
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Solution Overview
Problem
Current data backup methods are inefficient as they transfer all data blocks during incremental snapshots, leading to unnecessary data transfer and increased bandwidth usage, as they struggle to accurately identify unchanged data blocks.
Innovation Solution
Implementing a file system filter running in kernel mode to track changes between snapshots, using digital fingerprints to verify unchanged data blocks, and transferring only changed blocks, with the option to automatically transfer untagged blocks for backup.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all data blocks are transferred during incremental snapshots, then backup reliability is ensured, but bandwidth usage increases and transfer efficiency decreases
Solution Approach 1:
The patent extracts only the changed data blocks from the complete data set for transfer during incremental snapshots. By identifying and isolating only the modified blocks since the last snapshot, the system transfers minimal data while ensuring all changes are captured, thus maintaining backup reliability while reducing bandwidth consumption.
Solution Approach 2:
The system performs preliminary identification of changed data blocks before the actual transfer process. By pre-marking or tracking which blocks have changed since the last snapshot, the system prepares the minimal necessary data set for transfer in advance, avoiding unnecessary data movement while ensuring completeness of backup.
2Reliability
If tracking session starts before last snapshot and ends after current snapshot, then all changes are captured, but false positives increase requiring additional verification
Solution Approach 1:
The tracking session deliberately covers a broader time range (starting before and ending after the snapshot) to ensure no changes are missed. This excessive action captures all potential changes including false positives, which are then filtered through verification mechanisms like digital fingerprint comparison to identify only the actual changed blocks.
Solution Approach 2:
The system uses feedback mechanisms such as digital fingerprints or checksums to verify whether tracked blocks actually changed. After the tracking session identifies potential changes, the verification process compares the current state against the snapshot state, providing feedback that confirms or refutes the detected changes, thus eliminating false positives.
3Loss of energy
If unchanged data blocks are identified and not transferred, then bandwidth is saved, but identification accuracy must be high to avoid missing changes
Solution Approach 1:
The patent introduces intermediary verification mechanisms such as digital fingerprints, checksums, or metadata tags that act as mediators between the tracking session and the transfer decision. These intermediaries provide a reliable basis for identifying changed blocks without requiring direct comparison of entire data blocks, thus enabling accurate change detection with high confidence while minimizing false negatives.
Data Source
AI summary
In one approach, a set of data blocks or files is tracked for changes between snapshots. This may be done by a file system filter running in kernel mode. The data blocks or files that are tagged as unchanged are not transferred to backup because there is no need to update since the last backup. Other data blocks and files may be first tested for change, for example by comparing digital fingerprints of the current data versus the previously backed up data, before transferring to backup.