Amortized Snapshots Rolling Backup Depth
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Solution Overview
Problem
In computing environments where full backups are infrequent or incremental backups are frequent, the number of backups to maintain and the storage space required become large, making data restoration and rollback processes time-consuming due to the need to traverse numerous backups sequentially.
Innovation Solution
Implementing a system and method for rolling snapshot updates with a maximum depth, which involves obtaining a snapshot manifest, incrementing a snapshot count, and determining subsets of data objects to store incremental and proportional portions of the data, allowing for efficient data restoration without periodic full snapshots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If incremental backups are performed frequently, then data loss risk is reduced and data freshness is improved, but the number of backups to maintain and storage space required become large
Solution Approach 1:
The backup system segments the backup set into a current backup (most recent full or incremental backup) and a series of differential backups (changes since the current backup). This segmentation allows the system to maintain only a limited number of backups while still providing comprehensive data protection, as the differential backups reference the current backup rather than maintaining complete independent copies.
Solution Approach 2:
The patent introduces a temporal dimension to backup management by implementing a sliding window mechanism that limits the retention period of differential backups. Instead of maintaining all historical backups indefinitely, the system keeps differential backups only within a specified time window, thereby reducing storage requirements while maintaining data loss protection within acceptable parameters.
2Reliability
If incremental backups are performed frequently, then data freshness is improved, but storage space required becomes large
Solution Approach 1:
The system extracts and stores only the changes (differential data) since the current backup rather than storing complete backup copies. This extraction approach significantly reduces storage space requirements while maintaining data freshness, as only the modified portions of data are captured and stored in each incremental backup.
Solution Approach 2:
The system discards older differential backups that fall outside the sliding window period, as their data can be recovered by applying the sequence of retained differential backups from the current backup. This discarding mechanism frees up storage space while preserving the ability to restore data to any point within the retention window.
3Reliability
If numerous backups are maintained, then data restoration completeness is improved, but restoration time becomes long due to sequential traversal
Solution Approach 1:
The system performs preliminary organization of backup data by maintaining a clear hierarchical structure with a current backup and ordered differential backups. This preliminary structuring enables efficient restoration by allowing the system to start from the current backup and apply only the necessary differential backups within the sliding window, rather than traversing all historical backups sequentially.
Solution Approach 2:
The backup system implements dynamic adjustment of the sliding window size and retention policy based on storage capacity and restoration requirements. This dynamic behavior allows the system to optimize the balance between restoration completeness and restoration speed, adjusting the number of retained differential backups to match available resources and recovery point objectives.
Data Source
AI summary
A snapshot depth for a volume is obtained, with the snapshot depth indicating an upper limit to a number of snapshot updates, where an individual snapshot update of the snapshot updates includes data of the volume that has been modified since a time of storage of a previous snapshot update and an amount, based at least in part on the snapshot depth, of other data of the volume. The number of snapshot updates sufficient to construct a full snapshot of a state of the volume at a particular point in time is determined based at least in part on the snapshot depth, and at least the number of snapshot updates is caused to be stored.