Per-Extent Compression Selection for Target Data Reduction
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Solution Overview
Problem
Existing computing systems face challenges in efficiently achieving a target data reduction ratio while minimizing hardware resource consumption, as they lack dynamic selection mechanisms between different compression algorithms based on the specific characteristics of write operations.
Innovation Solution
A method and apparatus that dynamically select between multiple compression algorithms by predicting, on a per-extent basis, whether a write operation would benefit more from compression algorithm A or algorithm B, and selectively using algorithm B for write operations that are predicted to achieve greater compression benefits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If compression algorithm B is used for all write operations, then data reduction ratio is improved, but hardware resource consumption increases
Solution Approach 1:
The patent applies local quality by selecting different compression algorithms for different write operations based on their specific characteristics. The compression engine evaluates each write operation's compressibility using algorithm A and compares it with the extent's average compressibility ratio. This allows algorithm B to be applied locally only to write operations that exhibit greater compressibility, rather than uniformly applying it to all operations. This resolves the contradiction by achieving high data reduction ratios only where beneficial, thereby conserving hardware resources on operations where algorithm B would not provide significant improvement.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting the selection of compression algorithms based on computed compressibility parameters. The system calculates a compressibility ratio for each extent by comparing average compression results from algorithm A versus algorithm B. This parameter-driven approach allows the system to adaptively change which algorithm is applied to each write operation, optimizing the balance between data reduction ratio and resource consumption by selecting algorithm B only when the compressibility ratio indicates it will provide meaningful improvement.
2Loss of substance
If compression algorithm B is used for all write operations, then compressibility is improved, but computational expense increases
Solution Approach 1:
The patent implements partial action by applying compression algorithm B only to the subset of write operations that are predicted to benefit most from it, rather than applying it excessively to all operations. The system computes a compressibility ratio for each extent and compares individual write operation compressibility against this ratio. Algorithm B is applied only when the write operation's compressibility exceeds the extent's average, representing a partial application strategy that reduces computational expense while maintaining improved compressibility where it matters most.
Solution Approach 2:
The compression engine performs self-service by autonomously evaluating each write operation's characteristics and automatically selecting the appropriate compression algorithm without external intervention. The system computes compressibility metrics, compares them against stored extent averages, and makes algorithm selection decisions independently. This self-service mechanism eliminates the need for manual configuration or external control, allowing the system to optimally balance compressibility improvement against computational expense through automated, data-driven algorithm selection.
Data Source
AI summary
A method and apparatus for enabling dynamic selection between compression algorithms to achieve a target data reduction ratio is provided to predict, on a per-extent basis, whether a write operation targeting a piece of data on a particular extent would be preferentially compressed using either compression algorithm A or compression algorithm B. By selectively implementing compression of particular write operations using compression algorithm B, when it is predicted to achieve a greater compression benefits, it is possible to reduce the overall data reduction ratio achieved by the compression engine while economizing the amount of resources expended by the compression engine. The dynamic selection process determines a per-extent compressibility ratio, which is used in connection with a first threshold to evaluate write operations on a per-extent basis. The dynamic selection processes also determines a per-extent size threshold to select between compression algorithms for particular write operations.