SSD Zone-Based Compression for Hot and Cold Data Writes
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Solution Overview
Problem
Solid state drives (SSDs) lack efficient data classification, leading to reduced compression efficiency and increased write amplification factor due to random storage of hot and cold data, resulting in higher garbage collection frequencies.
Innovation Solution
A storage system that sets multiple zones in non-volatile memory based on data access attributes, compressing and storing data according to zone-specific methods to differentiate between hot, normal, and cold data, thereby optimizing storage efficiency and reducing garbage collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If data is randomly stored without classification, then storage simplicity is maintained, but compression efficiency is reduced and write amplification factor increases
Solution Approach 1:
The storage medium is divided into multiple zones (first zone, second zone, third zone) based on data access patterns. Hot data is stored in the first zone, normal data in the second zone, and cold data in the third zone. This segmentation allows different compression strategies to be applied to different zones, improving overall compression efficiency while maintaining manageable storage organization.
Solution Approach 2:
Different compression ratios are applied to different zones based on their specific characteristics. The first zone (hot data) uses a first compression ratio, the second zone (normal data) uses a second compression ratio, and the third zone (cold data) uses a third compression ratio. This local quality approach optimizes compression efficiency for each zone while balancing write amplification factors.
2Device complexity
If data is randomly stored without classification, then storage structure simplicity is maintained, but garbage collection frequency increases
Solution Approach 1:
The storage medium is segmented into multiple zones based on data access patterns. Hot data is stored in the first zone, normal data in the second zone, and cold data in the third zone. This segmentation isolates frequently accessed data from less accessed data, reducing the impact of garbage collection operations on overall system performance and reducing garbage collection frequency.
Solution Approach 2:
Data is pre-classified and stored in appropriate zones based on access patterns before garbage collection is needed. By proactively organizing data into hot, normal, and cold zones, the system prepares for future garbage collection operations, reducing the frequency and impact of these operations on system productivity.
3Device complexity
If uniform compression is applied to all data, then processing simplicity is maintained, but storage efficiency is reduced
Solution Approach 1:
Different compression ratios are applied to different zones based on their specific characteristics. The first zone (hot data) uses a first compression ratio, the second zone (normal data) uses a second compression ratio, and the third zone (cold data) uses a third compression ratio. This local quality approach optimizes storage efficiency for each zone while maintaining reasonable processing complexity through automated zone identification and compression ratio selection.
Solution Approach 2:
The compression ratio is dynamically adjusted based on the zone and data characteristics. Rather than using a static uniform compression ratio, the system dynamically selects appropriate compression ratios for different zones, improving overall storage efficiency while the processing circuitry automatically manages the complexity of determining and applying the appropriate compression ratio for each data block.
4Device complexity
If no data classification is performed, then access time simplicity is maintained, but input/output latency increases
Solution Approach 1:
The storage medium is segmented into multiple zones based on data access patterns. Hot data is stored in the first zone, normal data in the second zone, and cold data in the third zone. This segmentation improves input/output latency by locating frequently accessed hot data in a dedicated zone that can be quickly accessed, while the processing circuitry automatically identifies and manages zones based on logical addresses, maintaining reasonable access time simplicity.
Solution Approach 2:
Data is pre-classified and stored in appropriate zones based on access patterns before access is needed. By proactively organizing data into hot, normal, and cold zones, the system prepares for future access operations, reducing input/output latency when data is actually accessed while the automated zone management keeps access time simplicity reasonable.
Data Source
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AI summary
A storage device may include a non-volatile memory including a plurality of zones, the non-volatile memory configured to sequentially store data in at least one of the plurality of zones, and a processing circuitry configured to, receive a first write command and first data from a host, the first write command including a first logical address, identify a first zone of the plurality of zones based on the first logical address, compress the first data based on compression settings corresponding to the first zone, and write the compressed first data to the first zone.