Storage Snapshot Deletion Scoring for Space and Data Balance
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Solution Overview
Problem
Current snapshot auto-deletion policies in storage systems, which are based on creation time, fail to efficiently release storage space while minimizing data loss, as they do not account for the impact of deleting snapshots with varying block sharing and resource usage.
Innovation Solution
A method that determines an evaluation score for each snapshot based on reference count, storage resource size, and the number of blocks referenced only once, allowing for selective snapshot deletion to optimize space release and data retention, using a processing unit and memory to execute instructions for managing the storage system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If snapshots are deleted based on creation time policy, then storage space can be released, but data retention is compromised because snapshots with high block sharing are deleted
Solution Approach 1:
The patent changes the deletion selection parameter from creation time to an evaluation score that incorporates reference count, storage resource size, and number of uniquely referenced blocks. This parameter transformation enables optimization of both space release and data retention by selecting snapshots for deletion based on their actual impact rather than temporal criteria.
Solution Approach 2:
The system implements feedback by calculating an evaluation score for each snapshot that quantifies the impact of deletion on space release and data retention. This feedback mechanism allows the system to iteratively select snapshots for deletion that maximize space recovery while minimizing data loss, creating a closed-loop optimization process.
2Productivity
If snapshots with high block sharing are deleted to maximize space release, then storage efficiency improves, but data recovery capability deteriorates
Solution Approach 1:
The patent transforms the deletion selection criterion from simple block sharing metrics to a composite evaluation score that balances space release potential with data retention importance. This parameter change enables the system to identify snapshots that can be safely deleted without compromising data recovery capabilities.
Solution Approach 2:
The evaluation score provides feedback on the trade-off between space release and data recovery capability. By incorporating reference count and unique block information into the scoring mechanism, the system receives feedback that guides selection of snapshots whose deletion minimizes impact on data recoverability while maximizing space recovery.
3Loss of information
If all snapshots are retained to ensure data recovery, then data retention is maximized, but storage space is wasted
Solution Approach 1:
The patent introduces an evaluation score parameter that enables differentiation between snapshots based on their deletion impact. This parameter transformation allows the system to identify and delete only those snapshots whose removal has minimal impact on data retention, thereby freeing storage space while preserving data recovery capability for critical snapshots.
Solution Approach 2:
The system extracts and identifies specific snapshots for deletion based on evaluation scoring, separating them from the set of snapshots that should be retained. This extraction approach enables selective deletion of low-value snapshots (those with minimal data recovery importance) while preserving high-value snapshots, thus optimizing storage space without sacrificing data retention.
4Productivity
If evaluation scoring is implemented for each snapshot, then deletion optimization improves, but system complexity increases
Solution Approach 1:
The patent transforms the simple temporal deletion criterion into a multi-parameter evaluation score system. While this increases complexity, it enables optimized deletion decisions that balance multiple objectives (space release, data retention, recovery capability). The parameter transformation is justified by the significant improvement in deletion optimization and resource management efficiency.
Data Source
AI summary
Techniques involve managing a storage system. Along these lines, for each of a plurality of snapshots, a plurality of blocks referenced by the snapshot is determined. An evaluation score indicating an impact that deletion of the snapshot has on space release and data retention is determined based on at least one of: a reference count for the plurality of blocks, a size of a storage resource associated with the snapshot, and the number of blocks of the plurality of blocks that are referenced only once. At least one snapshot to be deleted is selected from the plurality of snapshots based on respective evaluation scores of the plurality of snapshots.