Hash-Based Entropy Detection for Single-Pass Data Reduction
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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 resources and power, especially when data blocks do not exhibit self-compression properties.
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 implementation flexibility through modular design.
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, the system can optimize processing decisions upfront, preventing unnecessary compression operations on incompressible data and reducing overall processing time.
2Productivity
If conventional compression functions are performed on all data blocks, then compression opportunities may be captured, but significant power and computational cycles are consumed especially on incompressible data
Solution Approach 1:
The system performs preliminary entropy detection using hash-based techniques to assess data compressibility before initiating full compression processing. This preliminary assessment allows the system to identify incompressible data blocks early and bypass expensive compression operations, significantly reducing power consumption while maintaining compression throughput for suitable data.
Solution Approach 2:
Instead of performing full compression on all data blocks, the system applies partial processing only to blocks that exhibit compressible characteristics. The entropy detection mechanism enables selective application of compression resources, avoiding excessive processing on incompressible data while capturing all available compression opportunities.
3Device complexity
If data blocks are processed in conventional size for compression, then processing simplicity is maintained, but line rate performance and speed requirements cannot be met
Solution Approach 1:
The system segments data into smaller fixed-size blocks that can be processed independently and in parallel. This segmentation enables the unified data path to handle multiple blocks simultaneously, achieving line rate performance while maintaining processing simplicity through consistent block-sized operations and parallel hash table lookups.
Solution Approach 2:
The system transitions from sequential processing to parallel processing by implementing multiple hash tables and parallel lookup mechanisms. This dimensional change in processing architecture enables simultaneous handling of multiple data blocks, achieving high-speed line rate performance without complicating the fundamental compression logic.
4Reliability
If compression checking is performed at the end of compression stage, then compression results can be verified, but resources have already been consumed and latency increases
Solution Approach 1:
The system performs verification checks during the compression process itself rather than after completion. Entropy detection and compressibility validation occur early in the single pass, allowing the system to verify compression suitability before committing full processing resources, thereby reducing verification latency while maintaining result reliability.
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.