Inline Data Entropy Compression via Pipelined Instructions
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Solution Overview
Problem
Conventional data compression in data storage systems is computationally expensive, making it inefficient for real-time processing, especially when dealing with high-entropy data blocks that are not compressible, as existing entropy calculations are costly and unsuitable for real-time decision-making.
Innovation Solution
An improved technique using pipelined instructions to generate entropy measures for data blocks in real-time, allowing the storage system to selectively compress low-entropy chunks while avoiding compression of high-entropy chunks, thereby optimizing data processing and reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional entropy calculation methods are used to determine compressibility, then accurate prediction of data reduction is achieved, but computational cost becomes too high for real-time processing
Solution Approach 1:
The patent segments the entropy calculation process into discrete pipelined stages that can be executed in parallel. Instead of performing a single complex entropy calculation on entire data blocks, the system divides data into smaller chunks and processes them through multiple pipeline stages simultaneously, enabling real-time entropy estimation without sacrificing accuracy.
Solution Approach 2:
The patent performs preliminary entropy estimation on data chunks before committing to full compression operations. By calculating entropy metrics in advance using efficient pipelined instructions, the system can identify compressible data early in the write path and avoid wasting computational resources on non-compressible high-entropy data.
2Quantity of substance
If compression is performed on all data blocks, then storage efficiency is improved, but computational resources are wasted on high-entropy uncompressible data
Solution Approach 1:
The patent implements a feedback mechanism where entropy calculation results directly control subsequent compression decisions. The entropy metric serves as feedback that informs the compression engine whether to proceed with compression operations, creating a closed-loop system that adapts to data characteristics and avoids wasting resources on unsuitable candidates.
Solution Approach 2:
The patent changes the parameter being measured from raw data to entropy metric. By transforming the problem from direct compression attempts to entropy-based prediction, the system can efficiently identify which data blocks are suitable for compression without actually performing the compression, thereby avoiding unnecessary computational expenditure.
3Loss of time
If entropy calculation is performed in-line with write requests, then compression decisions can be made on-the-fly, but computational overhead increases
Solution Approach 1:
The patent maintains continuous useful action by implementing entropy calculation as an integral part of the write path using pipelined instructions. Rather than performing entropy calculation as a separate batch operation, the system continuously computes entropy metrics alongside data ingestion, ensuring that compression decisions are always available when needed without adding significant overhead.
Data Source
AI summary
A technique for managing data storage obtains a batch of chunks of data. The technique generates, using multiple pipelined instructions operating on the batch, a measure of data entropy for each of the chunks in the batch. The technique selectively compresses chunks in the batch based at least in part on the measures of data entropy generated for the respective chunks.