Dynamic Backup Level Selection Based on Data Change
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Solution Overview
Problem
Existing backup technologies, particularly incremental backups, can be inefficient in terms of compute resources and recovery time objectives (RTO), especially when dealing with large data changes, leading to potential business discontinuities and increased Total Cost of Ownership (TCO) due to static selection algorithms that lack intelligence.
Innovation Solution
A system and method for intelligently and dynamically selecting backup levels, such as full, incremental, or differential backups, based on measuring the degree of data change since the last backup, using a rule engine to determine the appropriate backup level based on recovery time and other factors, thereby optimizing backup efficiency and reducing RTO.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If incremental backup is used, then storage efficiency is improved and data transmission is reduced, but compute resources are heavily consumed and recovery time increases
Solution Approach 1:
The system dynamically adjusts backup level selection based on current data change conditions, transitioning between incremental and full backups as needed. The backup level is not fixed but adapts to changing conditions, resolving the contradiction by making the backup strategy flexible rather than static.
Solution Approach 2:
The system changes the backup parameter (backup level) based on the degree of data change. When data change exceeds a threshold, the system switches from incremental to full backup mode, adjusting the backup parameter to match the actual data state and optimize both storage efficiency and recovery time.
2Loss of substance
If incremental backup is used, then storage efficiency is improved, but compute resources are heavily consumed to process changes
Solution Approach 1:
The system dynamically selects backup levels based on data change conditions, reducing compute resource consumption by avoiding incremental backup processing when data change is substantial. Instead of always processing changes, the system adapts to conditions and switches to full backup when appropriate.
Solution Approach 2:
The system uses full backups as a 'reset' mechanism when incremental backup becomes too costly in terms of compute resources. By periodically performing full backups when data change threshold is exceeded, the system discards the complex chain of incrementals and starts fresh, reducing cumulative compute overhead.
3Device complexity
If static backup level selection is used, then system complexity is reduced, but backup efficiency and adaptability deteriorate
Solution Approach 1:
The backup system performs self-assessment by calculating the degree of data change and automatically selecting the appropriate backup level without external intervention. The system serves itself by making intelligent decisions based on monitored conditions, improving efficiency while maintaining manageable complexity through automation.
Solution Approach 2:
The system continuously monitors data change conditions and uses this feedback to adjust backup level selection. By implementing a feedback loop that measures data change and responds with appropriate backup level adjustments, the system achieves adaptability and efficiency while keeping complexity controlled through rule-based decision logic.
Data Source
AI summary
A rule specifies a data change percent and a backup level to use corresponding to the data change percent. The backup level may be a full backup or a non-full backup. Before backing up a source for an impending backup, a percent of data change of the source from a last backup of the source to the impending backup is calculated. The rule is evaluated against the calculated percent change to select a backup level for the impending backup. Based on the evaluation, the full backup level or non-full backup level is selected and the source is backed up according to the selected backup level.