Storage Device Control Table Set Positioning
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Solution Overview
Problem
The increasing number of read and write operations in storage devices leads to a spike in control table data entries, making search operations time-consuming and requiring significant space for logical to physical mappings.
Innovation Solution
Designating a specific position for each control table set and storing updates in these designated positions to optimize data management and reduce latency, while dynamically determining when to perform read/write operations using volatile or non-volatile memory based on storage device state and workload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If logical to physical mappings are stored for each unit of data written, then data retrieval is enabled, but storage space requirements increase significantly
Solution Approach 1:
The control table is divided into multiple control table sets, each managing a specific range of logical addresses. This segmentation reduces the size of individual control tables, allowing efficient storage while maintaining complete mapping coverage across all data in the storage device.
Solution Approach 2:
The patent introduces a hierarchical structure with control table sets organized in groups, adding a dimensional organization layer. Each control table set contains control tables for specific logical address ranges, creating a structured multi-level system that optimizes both storage efficiency and retrieval speed.
2Measurement precision
If control table sets are updated continuously to track data movements, then mapping accuracy is maintained, but processing time increases
Solution Approach 1:
Control table sets are pre-allocated for specific logical address ranges before data is written. This preliminary organization allows the system to directly determine which control table set to update or access based on the logical address, avoiding time-consuming searches and maintaining mapping accuracy efficiently.
Solution Approach 2:
Each control table set is optimized for its specific logical address range, with control tables tailored to the local data distribution patterns. This local optimization allows efficient updates and searches within each range without affecting the entire control table structure, reducing overall processing time.
3Reliability
If control table sets are searched sequentially to find data, then all mappings can be checked, but search operations become time-consuming
Solution Approach 1:
The control table is segmented into multiple control table sets, each responsible for specific logical address ranges. This segmentation enables parallel or targeted searches within relevant ranges only, dramatically reducing search time while maintaining completeness through the organized structure that ensures all ranges are covered.
Solution Approach 2:
The hierarchical organization of control table sets creates a dimensional structure that allows the system to navigate to specific ranges efficiently. Instead of linear sequential search, the system can directly access relevant control table sets based on logical address ranges, adding a dimensional shortcut to the search process.
Data Source
AI summary
With ever-increasing capacities and performance demanded in new storage devices, the number of control table data entries are increased to store more updates stemming from the increased number of read and/or write operations. To avoid becoming a bottleneck, devices, such as storage devices, and other similar methods and systems as described herein efficiently manage control table sets to reduce latency. This can be accomplished by designating a specific position for each control table set and storing updates to such control table sets in a designated position. Furthermore, data can be efficiently kept in the volatile memory, such as SRAM, or evicted from the volatile memory to the non-volatile memory, such as NAND. Determinations can occur for when the read/write operations should be performed using volatile memory or non-volatile memory. These determinations can be decided dynamically and based on the storage device state and incoming workload, resulting in lower overall latencies.