Storage Device Timestamp Tracking for Selective Wear Leveling
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Solution Overview
Problem
Current storage devices lack the ability to classify and manage data based on usage patterns, leading to inefficient wear leveling and reduced storage lifespan due to uneven data access.
Innovation Solution
The storage device generates timestamp metadata to classify memory devices based on access frequency, allowing for processes such as compaction and data transfer to a cloud-based service to be triggered based on usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If wear leveling process is applied to distribute read and write cycles evenly across memory devices, then the lifespan of SSD is extended, but additional energy and processing cycles are consumed
Solution Approach 1:
The patent changes the parameter of data classification by introducing timestamp metadata to track when data was last accessed. This allows the system to identify cold data (infrequently accessed) versus hot data (frequently accessed), enabling selective wear leveling operations only on necessary memory devices, thereby reducing overall energy consumption while maintaining SSD lifespan extension
Solution Approach 2:
The patent applies local quality by differentiating treatment for different portions of the memory array based on their access patterns. Instead of uniformly applying wear leveling to all memory devices, the system selectively applies wear leveling only to memory devices containing hot data, while leaving cold data untouched, thus reducing processing cycles and energy consumption
2Duration of action of stationary object
If wear leveling process is applied to distribute read and write cycles evenly across memory devices, then the lifespan of SSD is extended, but processing overhead increases
Solution Approach 1:
The patent introduces timestamp metadata as a new parameter to track data access patterns. This enables the controller to classify data as cold or hot based on timestamps, allowing selective wear leveling operations. The timestamp parameter adds minimal processing overhead compared to full wear leveling while achieving the same lifespan extension goal
Solution Approach 2:
The patent applies partial action by performing wear leveling only on memory devices containing hot data rather than all memory devices. This selective approach reduces processing overhead and complexity while still achieving adequate lifespan extension for the critical hot data portion of the storage system
3Quantity of substance
If data is stored in a memory array without classification, then storage capacity is maximized, but wear leveling efficiency decreases due to uneven data access patterns
Solution Approach 1:
The patent applies local quality by classifying memory devices into different categories (cold data and hot data) based on their access patterns tracked via timestamps. This enables the wear leveling process to focus computational resources on memory devices containing hot data, improving wear leveling efficiency without sacrificing total storage capacity
Solution Approach 2:
The patent introduces timestamp metadata as a classification parameter to differentiate between cold and hot data. This parameter enables the controller to optimize wear leveling operations by identifying which memory devices require attention, thereby improving wear leveling efficiency while maintaining full storage capacity utilization
Data Source
AI summary
Storage devices include a memory array comprised of a plurality of memory devices. These memory devices typically have a plurality of metadata associated with them in addition to the data stored within. This metadata may include a timestamp indicating when the last host command, such as a read or write command, was processed on each of the memory devices. Using this timestamp metadata, the storage device can classify data based on how long it has been since last processed. Once known, the storage device can process memory devices that have not been processed after a certain period of time. This processing can include avoiding wear leveling, transferring data to an external cloud service, or compressing data, among others. During processing, such as compaction, timestamps associated with the data stored in each memory device can be transferred or otherwise associated with any destination memory device, thereby preserving the generated data classifications.