Deduplicating Backup Server Cloud Storage Integration
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Solution Overview
Problem
Existing backup systems face challenges in integrating deduplicated data with cloud storage due to difficulties in moving older backup images, as they may contain data referenced by newer images, leading to inefficient storage management and increased costs.
Innovation Solution
A method is introduced that detects trigger conditions, such as storage space thresholds, to select and migrate data from a deduplicating backup server to cloud storage, using metadata like access time and reference counts to determine which data to move or delete, ensuring efficient storage usage and cost management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If older backup images are moved to cloud storage to reduce local storage space, then storage space efficiency is improved, but data availability and restore reliability deteriorate because older images may contain data referenced by newer images
Solution Approach 1:
The backup data is segmented into two locations: local storage for actively referenced data and cloud storage for archived data. The system maintains a reference count for each data unit, using this metadata to determine which segments remain locally accessible and which can be archived, thereby resolving the contradiction between space efficiency and data availability.
Solution Approach 2:
The system introduces cloud storage as an intermediary layer between local storage and permanent archives. By using cloud storage as an intermediate destination for older backup images, the system can reduce local storage pressure while maintaining the ability to restore data on demand, thus resolving the reliability concern.
2Quantity of substance
If deduplication is used to improve storage efficiency, then storage space utilization is improved, but complexity of data management and integration with cloud storage worsens
Solution Approach 1:
The deduplication system performs self-service by automatically tracking reference counts for each data unit and autonomously determining which data should be archived to cloud storage. The system uses metadata (reference counts, timestamps) to make intelligent decisions about data migration without requiring manual intervention, thereby managing complexity automatically.
Solution Approach 2:
The system changes the parameter of data state from a simple binary (stored/not stored) to a multi-state system that considers reference counts, access timestamps, and storage location. This parameter transformation enables sophisticated data management that balances storage efficiency with cloud integration complexity.
3Loss of energy
If backup images are archived to cloud storage, then storage costs are reduced, but restore operation performance deteriorates due to increased data retrieval distance and time
Solution Approach 1:
The system applies local quality by differentiating the storage location based on data characteristics: frequently accessed or currently referenced data remains locally stored for fast access, while rarely accessed data is archived to cloud storage. This quality-based differentiation ensures that restore operations for active data remain fast while still achieving cost savings from cloud archiving.
Data Source
AI summary
Various systems and methods can integrate a deduplicating backup server with cloud storage. For example, one method involves detecting a trigger condition associated with a storage device that stores backup images for a backup server. The method then selects to move a unit of data from the storage device to a cloud storage system, in response to detecting the trigger condition. The method selects the unit of data based on a most recent time at which the unit of data was accessed during a restore operation. The selection of the unit of data can also be based on a number of references to the unit of data, in systems in which the storage device is a deduplicated storage device.