Affinity-Aware Prefetching for Backup Deduplication
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Solution Overview
Problem
Existing deduplication systems face challenges in achieving high deduplication rates, especially when implementing new backup policies, due to inefficiencies in prefetching and caching strategies.
Innovation Solution
The implementation of affinity-aware fingerprint prefetching using machine learning techniques to analyze existing backup information, identify patterns, and prefetch data containers with high affinity, thereby improving deduplication performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional prefetching strategies are used in deduplication systems, then the system can operate with simpler mechanisms, but the deduplication rate remains low especially for new backup policies
Solution Approach 1:
The system performs preliminary analysis of backup data patterns and pre-calculates affinity scores for data containers before the actual backup operation. This advance preparation enables the prefetching mechanism to quickly identify and retrieve high-affinity containers during the backup process, significantly improving deduplication rate for new policies without requiring complex real-time analysis
Solution Approach 2:
The patent replaces traditional mechanical prefetching approaches (based on simple time or size thresholds) with a machine learning-based affinity scoring system. This substitution uses algorithms to analyze data patterns and predict which containers are most likely to be duplicated, achieving superior deduplication performance without proportionally increasing system complexity
2Adaptability or versatility
If more backup data is stored to ensure coverage for new policies, then the system can handle diverse backup scenarios, but storage requirements increase
Solution Approach 1:
The system pre-analyzes and stores affinity score information for data containers during initial backup operations. This preliminary processing creates a knowledge base that enables the system to efficiently handle new backup policies by leveraging historical affinity patterns, reducing the need to store redundant backup copies for every possible policy scenario
Solution Approach 2:
The patent changes the parameter used for backup selection from simple metadata (time, size) to a computed affinity score derived from machine learning analysis. This parameter transformation enables the system to adapt to new backup policies more efficiently by identifying truly relevant historical backups, thereby reducing storage requirements while maintaining versatility
3Measurement precision
If comprehensive data analysis is performed to identify prefetch candidates, then the deduplication accuracy improves, but the processing time increases
Solution Approach 1:
The system segments the backup data analysis into distinct phases: initial pattern analysis, affinity score calculation, and runtime matching. By dividing the comprehensive analysis into these segments, the system can perform thorough accuracy-critical analysis during off-peak times and use the pre-computed results during actual backups, maintaining high accuracy while minimizing processing time impact
Solution Approach 2:
The system performs comprehensive data analysis and affinity score computation in advance, before the actual backup operation begins. This preliminary action allows the runtime system to simply match incoming data against pre-computed affinity scores, achieving high identification accuracy without the time penalty of performing comprehensive analysis during the backup process
Data Source
AI summary
Disclosed are techniques that provide for deduplication in an efficient and effective manner. For example, such methods, computer program products, and computer systems can include retrieving container information for a first one or more containers of a plurality of containers of one or more backup images (where the one or more backup images were produced under an existing backup policy), generating pre-processed container information (where the generating the pre-processed container information comprises performing data pre-processing on the container information), determining a plurality of container ranges for the first one or more containers, generating container range affinity information for the one or more backup images (where the generating the container range affinity information comprises performing a container range operation using the plurality of container ranges, and storing the container range affinity information in a container range data structure.