Storage Controller Data Attribute Updating for Hot-Cold Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing storage devices face inefficiencies in data storage and operational performance due to inaccurate attribution of data, leading to improper data management and resource allocation, as external devices often provide attributes that do not accurately reflect the actual update frequency of data.
Innovation Solution
The storage device includes a controller that self-updates the attribute of data based on the increment of the average program-erase cycle and overwrite status of memory blocks, using internal calculations to determine whether data is hot, cold, or warm, thereby optimizing data storage and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the storage device uses attributes provided by external devices to manage data, then the operation is simple, but the data storage efficiency and operational performance deteriorate due to inaccurate attribute attribution
Solution Approach 1:
The storage device performs self-service by autonomously determining data attributes based on its internal monitoring of program-erase cycles and overwrite status, rather than relying on external device provisions. The controller calculates the increment of average program-erase cycles and detects overwrite events to accurately classify data as hot, cold, or warm, enabling the system to self-optimize its storage management without external intervention
Solution Approach 2:
The system implements feedback by continuously monitoring the actual usage patterns of stored data through program-erase cycle counting and overwrite detection. This feedback mechanism allows the storage device to dynamically adjust attribute assignments based on real-world data behavior, ensuring that attribute classifications accurately reflect actual data characteristics and usage patterns
2Productivity
If the storage device self-updates data attributes based on program-erase cycle increment and overwrite status, then the data storage efficiency improves, but the device complexity increases
Solution Approach 1:
The attribute determination process is segmented into distinct monitoring components: one component tracks program-erase cycle increments, another detects overwrite status, and a third synthesizes this information to determine data attributes. This segmentation allows each component to perform its specific function independently, managing complexity through functional decomposition while achieving accurate attribute classification
Solution Approach 2:
The controller performs preliminary actions by pre-establishing monitoring mechanisms for program-erase cycles and overwrite events. These monitoring structures are set up in advance to automatically track data usage patterns, enabling the system to proactively determine attributes before data management operations are needed, rather than reacting to external attribute provisions
Data Source
AI summary
A storage device may write data to a memory as requested by an external device. Subsequently, the storage device may update an attribute of the data based on an increment of an average program-erase cycle of the memory and whether the data has been overwritten. The attribute of the data may be one of a plurality of candidate attributes and the plurality of candidate attributes may include hot, cold, and warm.