SSD Data Processing Using ML for Proactive I/O Management
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Solution Overview
Problem
Existing SSD management strategies are passive, leading to inefficiencies in buffer scheduling and garbage collection, resulting in longer data response times.
Innovation Solution
A data processing method for solid state drives that predicts future I/O information using machine learning, allowing the SSD processor to proactively manage resources such as buffer scheduling and garbage collection based on predicted results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If passive buffer scheduling and garbage collection are used, then the SSD management is simple to implement, but the data response time increases and performance deteriorates
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict future I/O operations before they occur. The system analyzes historical I/O patterns and generates predictions about upcoming read/write operations, allowing the SSD to proactively prepare buffer allocations and garbage collection schedules in advance, thereby reducing actual data response time without significantly increasing operational complexity
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring actual I/O operations and comparing them with predicted operations. The system uses this feedback to refine and update the machine learning models, creating a closed-loop control system that adapts to changing workloads and optimizes buffer scheduling and garbage collection decisions over time
2Device complexity
If passive garbage collection is executed, then the SSD controller resource requirements are low, but the garbage collection efficiency is insufficient
Solution Approach 1:
The patent applies preliminary action in garbage collection by predicting which memory blocks will become invalid in the future based on I/O pattern analysis. The system proactively performs garbage collection on these predicted blocks before they actually become stale, distributing GC operations more evenly across time and reducing peak-time performance degradation without requiring significant additional controller resources
Solution Approach 2:
The patent changes key parameters of the garbage collection process by using machine learning predictions to dynamically adjust GC priorities, block selection criteria, and timing decisions. Instead of fixed or purely reactive GC policies, the system adapts these parameters based on predicted workload patterns, significantly improving GC efficiency while maintaining manageable controller complexity
3Device complexity
If passive buffer scheduling is used, then the SSD management logic is simple, but the buffer utilization efficiency is low
Solution Approach 1:
The patent applies preliminary action in buffer scheduling by predicting future I/O operations and proactively allocating buffer resources in advance. The machine learning model forecasts upcoming read/write requests, allowing the SSD to pre-allocate appropriate buffer sizes and types before the actual I/O operations arrive, thereby improving buffer utilization efficiency without substantially increasing management logic complexity
Data Source
AI summary
Disclosed is a method for data processing applied to a solid state drive, a computer device and a computer-readable storage medium. The method includes acquiring an interface protocol command received by the solid state drive. The method also includes parsing the interface protocol command to obtain I/O information from the interface protocol command. The I/O information includes at least an I/O timestamp, an I/O type, and an I/O size. The method further includes invoking machine learning based on the I/O information to predict I/O information of a first future time period, so that a processor of the solid state drive is configured to proactively execute management functions according to the prediction results.