Solid-State Drive Management via LSTM I/O Prediction
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Solution Overview
Problem
The management of solid-state drives is largely subjective and experience-dependent, leading to inaccurate management strategies that can reduce read-write performance, prolong response times, and decrease service capabilities.
Innovation Solution
A data processing method that acquires historical I/O data, uses a prediction model, such as an LSTM model, to determine the data intensity of a solid-state drive within a future window period based on access cycles, and manages the drive accordingly, including cache management and garbage collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If technician-based management strategy is used, then operational flexibility is maintained, but management accuracy deteriorates due to subjective opinions and lack of data support
Solution Approach 1:
The patent replaces the mechanical system of technician-based manual management with an automated intelligent management system. The system automatically acquires historical I/O data, uses prediction models (such as LSTM neural networks) to forecast future data access patterns, and executes management decisions without human intervention, thereby eliminating subjective opinions while maintaining operational flexibility through automated adaptation.
Solution Approach 2:
The solid-state drive management system performs self-management by autonomously acquiring its own historical I/O data, analyzing access patterns, predicting future needs, and executing management decisions independently. The system serves itself by using its own operational data to guide its management strategy, eliminating the need for external technician intervention while maintaining accurate, data-driven decision-making.
2Productivity
If experience-based management strategy is used, then operational simplicity is maintained, but read-write performance deteriorates due to inaccurate management decisions
Solution Approach 1:
The system continuously acquires historical I/O data as feedback about actual drive usage patterns, feeds this data into prediction models to refine future forecasts, and adjusts management decisions based on the accuracy of predictions. This closed-loop feedback mechanism ensures that management strategies are continuously optimized based on real performance data, improving read-write performance while maintaining operational simplicity through automated adaptation.
Solution Approach 2:
The system performs preliminary actions by predicting future data access patterns before they occur and proactively adjusting management strategies in advance. The prediction model analyzes historical data to forecast future I/O needs, allowing the system to prepare appropriate management decisions beforehand, thereby improving read-write performance by avoiding reactive adjustments and maintaining operational simplicity through preemptive optimization.
3Reliability
If subjective management strategy is used, then implementation simplicity is maintained, but service capability deteriorates due to deviation from optimal management
Solution Approach 1:
The patent replaces subjective human judgment with objective automated prediction models that process historical I/O data through algorithms such as LSTM neural networks. This substitution eliminates the deviation from optimal management that occurs with subjective decisions, improving service capability reliability while managing system complexity through standardized, reproducible computational processes rather than human expertise.
Solution Approach 2:
The prediction model serves as an intermediary between historical I/O data and management decisions. It objectively translates raw operational data into actionable forecasts, eliminating the need for subjective interpretation while maintaining the complexity management necessary for optimal service capability. The intermediary model ensures consistent, data-driven decision-making that improves reliability without requiring complex human expertise.
Data Source
AI summary
A data processing method, apparatus, device, and readable storage medium are provided. The method includes: acquiring historical I/O data (S101), where the historical I/O data is data of a solid-state drive that is accessed within a preset time period; using a prediction model to learn the historical I/O data to obtain a prediction result (S102), where the prediction result includes a data intensity of the solid-state drive to be accessed within a future window period, and the future window period is determined according to a cycle in which the solid-state drive is accessed; and managing the solid-state drive according to the prediction result (S103).