Reverse Delta Object Storage for Cloud Data Retention Cost Control
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Solution Overview
Problem
Current data protection systems in public cloud object storage face inefficiencies in restoring the most recent version of data, as they either store fully changed data blocks or standard incremental changes, leading to high storage consumption and delayed restore times, and lack effective cost optimization for retention periods.
Innovation Solution
A system that organizes data into fixed-size sequential blocks, uses reverse delta objects for efficient incremental backups, and implements a data retention cost control component to specify retention and tiering periods, optimizing storage costs by storing only the most recent version and using reverse delta objects to synthesize older versions, thereby reducing compute resource usage and storage costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If standard incremental changes (forward deltas) are stored for data backup, then storage consumption is reduced, but restore time is significantly delayed
Solution Approach 1:
The patent inverts the traditional forward delta approach by using reverse delta objects. Instead of storing changes from an older version to a newer version (forward in time), the system stores changes from the current version backward to previous versions. This inversion enables faster restoration of the most recent version while maintaining storage efficiency, as the reverse delta objects can be applied directly to reconstruct previous states without requiring sequential application of multiple forward deltas.
2Speed
If fully changed data blocks are stored for disaster recovery, then restore speed is improved, but storage consumption increases
Solution Approach 1:
The patent extracts only the essential change information needed for restoration by using reverse delta objects. Rather than storing complete data blocks, the system extracts and stores only the differential changes (reverse deltas) that are necessary to reconstruct previous versions. This extraction approach maintains fast restore capability while significantly reducing the storage overhead compared to storing full data blocks.
3Reliability
If data is retained in public cloud object storage with standard tiering, then data protection is ensured, but storage costs are not optimized
Solution Approach 1:
The patent applies local quality by differentiating storage requirements for different data versions. The system places the most recent data version in high-performance, expensive storage tiers for fast access, while moving older versions to lower-cost storage tiers. This is achieved through the reverse delta object structure, which enables efficient retrieval of recent versions while allowing older versions to be stored more economically, thus optimizing the cost-performance tradeoff for data retention.
Data Source
AI summary
Controlling data retention cost in cloud-based object storage by first obtaining current storage prices from the cloud provider for their different available storage tiers, and receiving user input for different possible retention parameters identity of data source and the retention period per storage tier. A cost calculator calculates the cost per time unit for each storage tier based on the current provider-specified price and the user-specified period. The calculated costs are displayed to the user a UI in tabular and/or graphical form. The user can change the retention period or data source parameters, and any impact of such changes on the cost will be calculated in real time and reflected in the graphical display. If the user changes the retention period to force an immediate movement of data from one tier to another, a data mover control facilitates this movement in the cloud object storage.