Key-Value Storage Compression Rate Estimation
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Solution Overview
Problem
Current data storage technologies in non-volatile memory, such as flash memory, face challenges in optimizing storage space efficiency due to varying storage characteristics, leading to inefficiencies in data compression and management, particularly in key-value storage architectures.
Innovation Solution
A computing unit is coupled with non-volatile memory to estimate the average compression rate factor, dynamically updating it based on compression rates of multiple storage units, and prohibiting programming if storage space consumption exceeds a threshold, thereby optimizing data storage space through data compression in key-value storage architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is performed in background to optimize storage space, then storage space efficiency is improved, but storage space consumption estimation accuracy deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing compression rate factors for different data types and patterns. Before actual storage operations, the computing unit estimates storage space consumption using these pre-computed compression rate factors, allowing accurate prediction without requiring real-time compression feedback.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual compression performance and updating compression rate factor estimates. The computing unit compares estimated storage consumption with actual consumption, and adjusts compression rate factors accordingly to improve future estimation accuracy while maintaining storage efficiency.
2Adaptability or versatility
If average compression rate factor is dynamically updated based on multiple storage units, then storage management adaptability is improved, but computational complexity deteriorates
Solution Approach 1:
The system segments the storage system into multiple independent storage units, each with its own compression characteristics. The computing unit calculates compression rate factors for individual storage units separately, then aggregates them to determine the average compression rate factor. This segmentation allows adaptive storage management while keeping computational complexity manageable through localized calculations.
3Reliability
If programming is prohibited when storage space consumption exceeds threshold, then data loss is prevented, but productivity deteriorates
Solution Approach 1:
The system dynamically adjusts the storage space threshold based on current compression performance and storage unit availability. Rather than using a fixed threshold, the computing unit modifies the threshold adaptively, allowing data programming to continue when compression can effectively reduce storage consumption, while preventing programming when it would lead to data loss. This dynamic approach maintains reliability while maximizing productivity.
Data Source
AI summary
A key-value storage architecture with data compression is shown. A computing unit is configured to estimate the average compression rate factor of a non-volatile memory. The computing unit is further configured to estimate storage space consumption of the non-volatile memory based on the average compression rate factor, and programming of the non-volatile memory is prohibited if to the storage space consumption exceeds a predefined threshold. The average compression rate factor is dynamically updated, and is a weighted result of compression rate factors of several storage units of the non-volatile memory.