Single-Pass Entropy Detection for Compression and Deduplication
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Solution Overview
Problem
Conventional data reduction systems perform data compression and deduplication as separate steps, leading to latency and bandwidth penalties, and require significant cycles and power, with limited ability to predict compressibility of data blocks.
Innovation Solution
A unified data path that combines data compression and deduplication in a single pass, using smaller data blocks, parallel hash table lookups, and early compressibility prediction to minimize wasted effort and optimize system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If data compression and deduplication are performed as separate steps, then each function can be implemented independently, but system latency increases and bandwidth penalties occur
Solution Approach 1:
The patent combines data compression and deduplication into a single unified data path that processes data blocks simultaneously. The system performs both compression encoding and deduplication hashing in parallel within the same processing pipeline, eliminating the sequential execution overhead and reducing system latency while maintaining independent functional capabilities through modular design components.
Solution Approach 2:
The system performs preliminary entropy detection and compressibility assessment on data blocks before full compression processing. By evaluating data characteristics early in the single-pass pipeline, the system can optimize processing paths and avoid unnecessary compression operations on incompressible data, thereby reducing overall processing time and latency.
2Reliability
If conventional compression functions are performed on all data blocks, then compression opportunities are not missed, but significant cycles and power are consumed
Solution Approach 1:
The patent implements partial compression action by performing lightweight entropy detection on all data blocks but applying full compression processing only to blocks identified as compressible. This selective approach ensures that compression opportunities are reliably detected while avoiding excessive power consumption on incompressible data blocks that would waste processing resources.
Solution Approach 2:
The system performs self-assessment of data block compressibility through entropy detection mechanisms that evaluate data characteristics independently. This self-service capability allows the compression system to autonomously identify which data blocks warrant full processing, optimizing the balance between reliable compression opportunity detection and energy-efficient resource allocation.
3Ease of operation
If compression processing is performed before checking compressibility, then all data is processed uniformly, but resources are wasted on incompressible data
Solution Approach 1:
The patent performs preliminary entropy detection and compressibility evaluation on data blocks before initiating full compression processing. This preliminary assessment uniformely applies to all incoming data blocks, maintaining ease of operation through consistent processing flow, while simultaneously preventing resource waste by identifying and skipping incompressible blocks before expensive compression operations are executed.
4Productivity
If larger data blocks are used for compression, then processing efficiency improves, but the ability to detect compressibility patterns decreases
Solution Approach 1:
The patent segments data into optimized block sizes that balance processing efficiency with compressibility detection accuracy. By dividing data streams into appropriately sized chunks, the system maintains high processing throughput while ensuring each block is small enough to exhibit detectable compressibility patterns. This segmentation enables both fast processing and accurate entropy measurement.
Solution Approach 2:
The system dynamically adjusts data block processing parameters based on detected compressibility characteristics. When high compressibility is detected, the system may consolidate or merge blocks to improve processing efficiency, while maintaining the ability to detect patterns at appropriate granularities. This dynamic adaptation allows the system to optimize the trade-off between processing speed and detection precision based on actual data characteristics.
Data Source
AI summary
Embodiments of the present invention include a memory unit and a processor coupled to a memory unit. The processor is operable to group a plurality of subsets of data from an input data stream and compute a first hash value corresponding to a first grouped subset of data. Additionally, the processor is operable to detect a match between the first hash value and a second hash value stored in a hash table. Furthermore, the processor is also configured to monitor a hash value match frequency for the input data stream in which the processor is operable to increment a counter value responsive to a detection of the match and determine an entropy level for the input data stream based on the counter value relative to a frequent hash value match threshold. The processor can generate an instruction to either initialize performance of a data compression operation when the counter value meets or exceeds the frequent hash value match threshold or refrain from the performance of the data compression operation when the counter value fails to meet the frequent hash value match threshold.