Dynamic Backup Strategy for Storage Management
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Solution Overview
Problem
Conventional storage management methods employ fixed backup strategies that fail to efficiently manage data sources with varying data change rates and recovery time objectives, leading to inefficiencies in backup operations and potential failure to meet recovery time objectives.
Innovation Solution
A method that acquires the data change rate and recovery capability of a backup system to dynamically determine a backup strategy, adjusting the frequency and type of backups such as full, differential, and incremental backups based on these factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a full backup strategy is used, then the recovery time objective is improved, but the backup time and backup space increase
Solution Approach 1:
The patent implements dynamic backup strategies that adapt to changing data conditions. The system continuously monitors data change rates and adjusts backup frequency and type accordingly, transitioning between full, differential, and incremental backups based on real-time conditions rather than using fixed schedules.
Solution Approach 2:
The system changes backup parameters (frequency, type, retention period) based on data characteristics. By analyzing data change rates and applying machine learning models, the system optimizes backup parameters to achieve the desired recovery time objective while minimizing backup time and storage consumption.
2Loss of time
If a non-full backup strategy is used, then the backup time and backup space are reduced, but the recovery time objective worsens
Solution Approach 1:
The system dynamically selects between full and non-full backup strategies based on real-time data change rates. When data changes slowly, incremental backups are used to minimize time and space. When data changes rapidly or recovery time requirements tighten, the system transitions to full backups to ensure recovery objectives are met.
Solution Approach 2:
The system incorporates feedback loops that monitor backup performance and recovery time objectives. Machine learning models analyze historical backup data and current system state to predict optimal backup strategies, continuously adjusting the approach based on whether recovery time objectives are being met.
3Device complexity
If fixed backup strategies are used, then the system complexity is reduced, but the backup efficiency and ability to meet recovery time objectives deteriorate
Solution Approach 1:
The system performs self-optimization through automated monitoring and machine learning models that independently analyze data patterns and adjust backup strategies without manual intervention. The system serves itself by automatically tuning backup parameters based on observed data characteristics and performance metrics.
Solution Approach 2:
The system automatically adjusts backup parameters including frequency, type, and retention periods based on data change rates and recovery time objectives. This dynamic parameter adjustment improves backup efficiency and ensures recovery objectives are met without requiring complex manual configuration or fixed rigid schedules.
Data Source
AI summary
A method, electronic device, and computer-readable medium for storage management is disclosed. The method for storage management includes acquiring a data change rate of a data source, the data change rate indicating an occurrence rate of data to be backed up in the data source. The method also includes acquiring a recovery capability of a backup system to recover backed-up data and determining, based on the data change rate and the recovery capability, a backup strategy for backing up the data to be backed up.