Storage Device Compression with Algorithm Fallback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing storage devices face inefficiencies in data storage and reduced lifespan due to the lack of effective data compression mechanisms, leading to suboptimal utilization of memory resources.
Innovation Solution
A storage device equipped with a controller that receives a compression write command and compresses data using a supported compression algorithm or a preset reference algorithm, reducing data size before storage, thereby enhancing efficiency and extending device lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored without compression, then storage operations are simple and fast, but storage capacity utilization is low and device lifespan is reduced
Solution Approach 1:
The storage device performs compression operations autonomously without requiring external device intervention. The controller automatically detects whether to apply compression based on command types, enabling the system to self-manage compression tasks and improve capacity utilization without adding operational complexity for users
Solution Approach 2:
The system dynamically changes the data state by applying compression algorithms that transform original data into compressed representations. By adjusting compression parameters based on command types and data characteristics, the system optimizes storage capacity utilization while managing processing complexity
2Quantity of substance
If compression algorithms are always applied, then storage capacity is optimized, but processing time and energy consumption increase
Solution Approach 1:
The system applies compression selectively rather than universally - it performs compression only for specific command types (e.g., normal write commands) while skipping compression for other commands (e.g., sequential write commands). This partial application of compression reduces processing time overhead while still achieving significant storage capacity optimization for appropriate data types
Solution Approach 2:
The compression application is made dynamic by adjusting compression behavior based on real-time conditions such as command types, data characteristics, and device state. The controller dynamically determines whether to compress data on a per-command basis, optimizing the balance between processing time and storage capacity utilization
3Adaptability or versatility
If multiple compression algorithms are supported, then adaptability is improved, but device complexity increases
Solution Approach 1:
The controller is designed with multi-functionality to support multiple compression algorithms (e.g., LZ4, ZLIB) within a single unified architecture. This universal design enables the device to adapt to different data types and external device requirements while managing complexity through a standardized control flow that handles all algorithms through the same decision-making process
Data Source
AI summary
A storage device may include a memory storing data and a controller. Such a controller may receive a compression write command from an external device to compress and write original data, compress the original data, based on whether compression information that corresponds to the compression write command is supported, into compressed data using a preset value, and store the compressed data in the memory.