Cache Segmentation for Data Storage Hit Ratio
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Solution Overview
Problem
Data storage devices for portable electronic devices face challenges in efficiently caching read-requested data, leading to suboptimal operation speed and hit ratios due to the lack of effective data management strategies.
Innovation Solution
A data storage device and operating method that divide the cache into multiple areas, group logical addresses, and allocate indexes to improve data caching efficiency by matching read-requested addresses to appropriate cache areas based on empty space availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data storage devices use simple caching without effective management strategies, then device complexity is reduced, but operation speed and hit ratio deteriorate
Solution Approach 1:
The cache is divided into multiple cache areas, each capable of storing data for different logical address ranges. This segmentation allows parallel caching operations and improves hit ratio by directing read requests to appropriate cache areas based on logical address grouping, thereby enhancing operation speed without requiring complex global cache management
Solution Approach 2:
The system changes the parameter of cache organization by introducing logical address groups and their corresponding indexes. Each cache area is associated with specific logical address groups through index matching, transforming the cache management approach from unified to structured, which improves both hit ratio and operation speed while maintaining manageable complexity through systematic parameter organization
2Reliability
If the cache is divided into multiple areas with logical address grouping, then hit ratio is improved, but device complexity increases
Solution Approach 1:
The cache is segmented into multiple cache areas, each dedicated to specific logical address groups. This segmentation improves hit ratio by reducing cache conflicts and enabling targeted caching, while the structured segmentation pattern keeps complexity manageable through systematic organization
Solution Approach 2:
Logical address groups and their indexes act as intermediaries between read requests and cache areas. The index matching mechanism serves as a mediator that automatically directs requests to the appropriate cache area, improving hit ratio while abstracting the complexity of cache management from the host system
3Productivity
If logical addresses are grouped and indexes are allocated, then data caching efficiency is improved, but management complexity increases
Solution Approach 1:
The system introduces logical address groups as a new parameter layer between physical addresses and cache locations. By grouping logical addresses and allocating indexes to these groups, the system transforms address management into a structured process that improves caching efficiency through predictable patterns while maintaining manageable complexity through systematic parameter organization
Solution Approach 2:
Each cache area is assigned specific local quality characteristics through its association with particular logical address groups via index matching. This local quality optimization allows each cache area to be specialized for specific address ranges, improving overall caching efficiency while the modular structure keeps management complexity localized and manageable
Data Source
AI summary
A method for operating a data storage device includes: dividing a cache into a plurality of cache areas; grouping a plurality of logical addresses into a plurality of logical address groups; allocating indexes to the respective logical address groups; and matching a read-requested first logical address set, a first cache area where data corresponding to the first logical address set are cached and an empty size of the first cache area, to an index corresponding to a logical address group to which the first logical address set belongs.