SSD Metadata Recovery via Dynamic CPU Allocation
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Solution Overview
Problem
Current data recovery methods in SSDs are inefficient and slow due to the reliance on a single CPU to recover metadata, which can lead to prolonged recovery times when the device is powered on.
Innovation Solution
A method where the data volume of each metadata type is assessed, and corresponding numbers of CPUs are allocated based on the volume, allowing multiple CPUs to recover metadata simultaneously, thereby improving recovery efficiency and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a single CPU is used to recover metadata, then device simplicity is maintained, but metadata recovery speed is slow
Solution Approach 1:
The patent segments the metadata recovery task by dividing different types of metadata (e.g., L2P table, primary table, secondary table) and assigning them to different CPUs for parallel processing. This segmentation enables simultaneous recovery of multiple metadata types, significantly improving recovery speed while maintaining manageable system complexity through structured task distribution.
2Productivity
If multiple CPUs are allocated to recover metadata, then recovery efficiency is improved, but resource allocation complexity increases
Solution Approach 1:
The patent applies local quality by allocating different numbers of CPUs to different metadata types based on their specific recovery needs and data volumes. For example, L2P table recovery may be assigned more CPUs due to its larger size, while other metadata types use fewer CPUs. This differentiated allocation optimizes recovery efficiency for each metadata type without requiring uniform complex management across all types.
Solution Approach 2:
The system dynamically adjusts CPU allocation parameters based on metadata characteristics such as data volume and recovery priority. By changing the number of CPUs assigned to each metadata type according to these parameters, the system achieves high productivity while keeping allocation management adaptable rather than statically complex.
3Loss of time
If uniform CPU allocation is used for all metadata types, then allocation simplicity is maintained, but recovery time is prolonged
Solution Approach 1:
The patent implements dynamic CPU allocation where the number of CPUs assigned to each metadata type can be adjusted based on real-time recovery needs and metadata characteristics. This dynamic approach allows the system to optimize recovery time by allocating more resources to critical or larger metadata types while maintaining simpler allocation for smaller types, reducing overall recovery time without requiring permanently complex allocation structures.
Data Source
AI summary
Provided are a data recovery method, system and apparatus and a storage device. In the present disclosure, a data volume of metadata of each type is acquired; a corresponding quantity of CPUs are allocated to the metadata of each type according to the data volume thereof; and when the storage device is powered on, each CPU is controlled to recover the metadata of the type corresponding thereto. In the present disclosure, by means of acquiring the data volume of the metadata of each type and allocating different numbers of CPUs to the metadata according to different data volumes thereof, when the storage device is powered on and the metadata in the storage device needs to be recovered, multiple CPUs are controlled to recover the metadata of the types corresponding thereto, so that the metadata recovery efficiency and speed may be improved.