Data Compression Deduplication Context Optimization
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Solution Overview
Problem
Existing mechanisms face challenges in efficiently performing both deduplication and compression on data segments, as deduplication algorithms work better with small segments while compression algorithms perform better with large segments, leading to inefficiencies in resource management and storage optimization.
Innovation Solution
The approach involves using a processor to apply the same compression context to multiple deduplicated data segments as if they were one segment, optimizing segment sizes based on file type and access frequency, and employing a work partitioning scheme using hash value ranges to improve deduplication efficiency and compression ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data segments are divided into small segments for deduplication, then deduplication efficiency is improved, but compression efficiency deteriorates
Solution Approach 1:
The patent applies segmentation by dividing data into small fixed-size segments (e.g., 4KB) for deduplication processing. Each segment is independently hashed and compared to identify duplicates. This segmentation enables efficient deduplication by creating manageable units that can be quickly processed and compared, directly improving deduplication efficiency while maintaining the ability to apply compression at the segment level.
Solution Approach 2:
The patent merges multiple small deduplicated segments into a single compressed data structure. After deduplication identifies and removes duplicate segments, the remaining unique segments are combined and compressed together using compression algorithms. This merging approach allows compression to work effectively on aggregated data while the deduplication process had already reduced redundancy, thereby resolving the contradiction between small segment size for deduplication and large data volume for compression.
2Loss of energy
If data segments are combined into large segments for compression, then compression efficiency is improved, but deduplication efficiency deteriorates
Solution Approach 1:
The patent applies preliminary action by performing deduplication on small segments before compression. The process first divides data into small segments, computes hashes, identifies duplicates, and removes them. Only after this preliminary deduplication step are the remaining segments compressed. This preliminary deduplication ensures that compression operates on already optimized data, eliminating the need to process large volumes of redundant data during compression, thus maintaining both deduplication and compression efficiency.
Solution Approach 2:
The patent maintains segmentation by keeping data divided into small fixed-size segments throughout the process. Even during compression, the segments remain as discrete units rather than being merged into large continuous blocks. This segmentation allows the system to benefit from both small segment deduplication efficiency and effective compression by applying compression algorithms to the collection of small segments, preserving the advantages of segment-based processing.
3Ease of manufacture
If fixed segment size is used for deduplication, then processing simplicity is improved, but compression adaptability deteriorates
Solution Approach 1:
The patent applies parameter changes by adjusting segment size based on file characteristics and compression requirements. While maintaining a default fixed segment size (e.g., 4KB) for simplicity, the system can modify segment parameters such as size, boundaries, or grouping strategies depending on the specific file type, access patterns, and compression algorithm being used. This flexibility allows optimization for different scenarios while preserving the simplicity of fixed-size processing when applicable.
Solution Approach 2:
The patent introduces dynamics by making the segmentation strategy adaptable rather than completely static. The system can dynamically adjust segment boundaries, sizes, or grouping based on file type, access frequency, and compression performance metrics. This dynamic approach allows the same basic fixed-size segment framework to adapt to different compression requirements and data characteristics, resolving the contradiction between processing simplicity and compression adaptability.
Data Source
AI summary
Mechanisms are provided for performing efficient compression and deduplication of data segments. Compression algorithms are learning algorithms that perform better when data segments are large. Deduplication algorithms, however, perform better when data segments are small, as more duplicate small segments are likely to exist. As an optimizer is processing and storing data segments, the optimizer applies the same compression context to compress multiple individual deduplicated data segments as though they are one segment. By compressing deduplicated data segments together within the same context, data reduction can be improved for both deduplication and compression. Mechanisms are applied to compensate for possible performance degradation.