Multi-Block Aggregate Compression for Storage Space Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing storage systems operating on 8K block granularity often perform compression on individual blocks, which is less space-efficient compared to compressing multiple blocks together, as evidenced by analysis showing limited compression savings for smaller block sizes and significant savings only when aggregating larger blocks.
Innovation Solution
A method that identifies blocks and determines whether to compress them based on heuristics such as distance metrics and digital entropy values, allowing for multi-block aggregation and compression, with different compression levels applied during background and non-background operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If compression is performed on individual 8K blocks, then the processing complexity is low, but the space efficiency is poor
Solution Approach 1:
The patent merges multiple individual 8K blocks into larger aggregate units before compression. By combining adjacent blocks that share common data patterns, the system achieves better compression ratios while keeping processing manageable through selective aggregation based on data similarity metrics.
Solution Approach 2:
The patent segments the storage space into different aggregation levels, allowing some regions to be compressed as individual blocks while others are aggregated into multi-block units. This segmentation enables the system to optimize for both low complexity and high space efficiency in different data regions.
2Quantity of substance
If multi-block aggregation compression is applied, then the space efficiency is improved, but the processing complexity increases
Solution Approach 1:
The patent applies different compression aggregation strategies to different local regions based on data characteristics. High-similarity regions are aggregated into multi-block units for maximum space efficiency, while low-similarity regions remain as individual blocks, creating a locally optimized compression structure.
Solution Approach 2:
The patent dynamically adjusts aggregation parameters such as aggregate size and compression threshold based on data entropy and similarity metrics. This allows the system to adapt the processing complexity to match the actual compression benefits, avoiding unnecessary complexity in regions where aggregation provides minimal improvement.
3Quantity of substance
If blocks with similar digital entropy values are compressed together, then the compression savings are maximized, but the selection process becomes more complex
Solution Approach 1:
The patent implements self-service by using the data's own digital entropy values to guide the aggregation process. Blocks automatically group with neighbors of similar entropy characteristics without requiring complex external analysis, allowing the data to direct its own optimal compression structure.
Solution Approach 2:
The patent uses feedback from digital entropy calculations to iteratively refine aggregation groups. The entropy values of compressed blocks feed back into the selection process, allowing the system to identify and correct suboptimal aggregations and continuously improve compression savings through adaptive re-grouping.
Data Source
AI summary
A method, computer program product, and computer system for identifying a plurality of blocks. At least one heuristic associated with at least a portion of the plurality of blocks may be determined. It may be determined whether to compress at least the portion of the plurality of blocks based upon, at least in part, the at least one heuristic. At least the portion of the plurality of blocks may be compressed based upon, at least in part, the at least one heuristic.