Memory Compression Using Deduplication and Value Locality
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Solution Overview
Problem
Existing data compression methods in computer memories face challenges in efficiently eliminating duplicates and value redundancy, leading to high metadata overhead and memory latency, especially when data objects change dynamically.
Innovation Solution
A computer memory compression method and device that analyzes for duplicate objects and value redundancy, encodes unique objects using data value locality, and provides metadata for efficient location and compression, combining deduplication with value-locality-based compression techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If deduplication is applied to memory blocks with reference pointers to unique copies, then compression ratio is improved, but metadata overhead increases significantly
Solution Approach 1:
The patent extracts and eliminates value redundancy from memory blocks by identifying and removing duplicate data values, keeping only unique values. This is achieved through value-locality-based compression that analyzes and compresses based on the distribution and repetition of actual data values within blocks, rather than treating entire blocks as atomic units with reference pointers.
Solution Approach 2:
The patent applies different compression strategies to different parts of the data based on value locality characteristics. By analyzing the specific value distribution within each memory block and applying targeted compression to regions with high value redundancy, it achieves efficient compression without requiring global metadata structures like reference pointers across entire blocks.
2Quantity of substance
If value-locality-based compression is applied to unique memory objects, then compressibility is improved, but metadata overhead and memory latency increase
Solution Approach 1:
The patent segments the compression process into efficient stages: identifying value redundancy patterns, encoding unique values, and managing metadata. By organizing metadata to reflect the segmented structure of compressed data and using efficient encoding schemes, it reduces the time penalty associated with compression and decompression operations while maintaining high compressibility.
3Quantity of substance
If compression techniques are applied to increase memory capacity, then storage efficiency is improved, but hardware costs and energy consumption increase
Solution Approach 1:
The patent implements self-service compression where the system automatically identifies and compresses value redundancy in memory blocks without requiring external intervention or complex hardware assistance. The value-locality-based compression algorithm operates autonomously to reduce memory usage and energy consumption, making the memory system self-optimizing.
Data Source
AI summary
A computer memory compression method involves analyzing computer memory content with respect to occurrence of duplicate memory objects as well as value redundancy of data values in unique memory objects. The computer memory content is encoded by eliminating the duplicate memory objects and compressing each remaining unique memory object by exploiting data value locality of the data values thereof. Metadata is provided to represent the memory objects of the encoded computer memory content. The metadata reflects eliminated duplicate memory objects, remaining unique memory objects as well as a type of compression used for compressing each remaining unique memory object. A memory object in the encoded computer memory content is located using the metadata.