SSD Data Classification for Garbage Collection Efficiency
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Solution Overview
Problem
Existing solid state drives (SSDs) face inefficiencies due to the mixing of sequential and random data during garbage collection, leading to early triggering of garbage collection and increased memory block erasures, which reduces system efficiency and causes fragmentation.
Innovation Solution
A data classifier module calculates a placement factor for streams of data based on a metadata update and recency count table, classifying data as hot, warm, or cold, and routes it to corresponding memory blocks, optimizing garbage collection procedures and reducing block erasures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sequential and random data are routed to the same memory block during garbage collection, then garbage collection can proceed, but data mixing creates fragmented memory blocks and triggers early garbage collection
Solution Approach 1:
The patent segments memory blocks into three distinct types: hot open memory blocks for frequently written data, warm open memory blocks for moderately written data, and cold open memory blocks for rarely written data. This segmentation prevents mixing of sequential and random data in the same block, maintaining data integrity while enabling efficient garbage collection in each segment.
Solution Approach 2:
The patent applies local quality by assigning different characteristics to different parts of the memory system. Each memory block type is optimized for specific data patterns: hot blocks handle frequent writes, warm blocks handle moderate writes, and cold blocks handle rare writes. This localized optimization resolves the contradiction by ensuring each block maintains its integrity while serving its specific purpose.
2Productivity
If data from multiple applications with different write patterns are stored together, then storage capacity is utilized, but frequent overwrites invalidate data and create fragmentation
Solution Approach 1:
The patent segments data storage by application write patterns, creating separate hot, warm, and cold data streams. Applications with frequent write patterns are routed to hot blocks, while applications with rare write patterns are routed to cold blocks. This prevents mixing of data with conflicting validity requirements, maintaining data stability while utilizing storage capacity efficiently.
Solution Approach 2:
The patent applies local quality by matching data characteristics to appropriate storage locations. Data from applications with frequent overwrite patterns is stored in hot blocks designed to handle such patterns, while data from applications with stable write patterns is stored in cold blocks. This localized matching maintains data validity within each storage region.
3Productivity
If garbage collection is triggered early due to fragmentation, then memory blocks can be reclaimed, but the number of block erasures increases
Solution Approach 1:
The patent segments garbage collection operations into three separate processes: hot garbage collection for hot blocks, warm garbage collection for warm blocks, and cold garbage collection for cold blocks. This segmentation allows each collection process to operate on data with similar characteristics, reducing fragmentation and the need for early collection, thereby reducing block erasures while maintaining reclamation speed.
Solution Approach 2:
The patent applies local quality by optimizing garbage collection parameters for each memory block type. Hot blocks undergo frequent but small-scale collection, warm blocks undergo moderate collection, and cold blocks undergo infrequent but comprehensive collection. This localized optimization reduces unnecessary erasures while maintaining efficient memory reclamation.
Data Source
AI summary
Methods for classifying data in a storage device are provided. A data classifier module in a controller calculates a placement factor of one or more streams of data associated with one or more logical block addresses based on a metadata update and recency count table. The data classifier module then classifies the one or more streams of data associated with one or more logical block addresses as hot, warm, or cold streams of data. Hot streams of data are routed to hot open memory blocks, warm streams of data are routed to warm open memory blocks, and cold streams of data are routed to cold open memory blocks. Routing streams of data to hot, warm, or cold open memory blocks results in more efficient garbage collection procedures and the reduction of block erasures.