SSD Throttling Delay Optimization via Machine Learning
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Solution Overview
Problem
Existing semiconductor memory devices, such as SSDs, struggle to set an optimal throttling latency for each workload or state, leading to inefficient performance and resource wastage.
Innovation Solution
A solid state drive (SSD) equipped with a throttling method that uses machine learning models to determine an optimal throttling delay time based on workload and state information, with a weight learning device adjusting the model weights for maximum performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed throttling delay time is used in SSD, then the device complexity is reduced and ease of operation is improved, but the productivity and quality of service deteriorate due to inability to adapt to varying workloads
Solution Approach 1:
The patent implements dynamic throttling delay adjustment by training a machine learning model to predict optimal delay times based on real-time workload characteristics and SSD internal states. The system transitions from a fixed, static throttling mechanism to a dynamic one that adapts to varying workloads, thereby improving productivity and QoS while maintaining ease of operation through automated model inference.
Solution Approach 2:
The patent changes the parameter of throttling delay time from a fixed value to a variable determined by machine learning model predictions. The model learns optimal delay parameters based on workload patterns and SSD states, allowing the system to adjust the throttling delay parameter dynamically to maximize productivity while preserving ease of operation through automated parameter optimization.
2Productivity
If machine learning model is introduced to determine optimal throttling delay time, then the productivity and quality of service are improved, but the device complexity increases due to additional processing requirements
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model offline using historical workload data and SSD performance metrics. The model is trained beforehand to learn the relationship between workload characteristics, SSD states, and optimal throttling delay times. During runtime, the pre-trained model performs fast inference to determine throttling delays, thereby improving productivity without significantly increasing runtime device complexity.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between workload input and throttling delay output. The model processes complex workload patterns and SSD state information to generate optimal throttling decisions, thereby improving productivity while containing device complexity by encapsulating the complexity within the model's internal structure rather than requiring complex real-time control logic.
3Reliability
If throttling delay time is increased to improve quality of service, then the reliability is improved, but the loss of time increases due to longer completion message delays
Solution Approach 1:
The patent optimizes the throttling delay parameter by using machine learning to predict the minimal delay required to achieve reliable SSD operation under different workload conditions. The model learns the optimal delay parameter that balances reliability requirements with time loss, adjusting the delay parameter dynamically rather than using a fixed conservative value, thereby improving reliability while minimizing time loss.
Solution Approach 2:
The patent implements feedback by using the trained machine learning model to continuously predict optimal throttling delays based on current workload and SSD state. The system monitors performance metrics and uses this feedback to adjust throttling delay predictions, ensuring that the delay is sufficient to maintain reliability while minimizing unnecessary time loss through adaptive optimization.
Data Source
AI summary
A throttling method for a storage device is provided. The throttling method includes: receiving a write command from a host; identifying, using a first machine learning model, a throttling delay time; transmitting a completion message to the host according to the throttling delay time; collecting weights of the first machine learning model and performance information of the storage device corresponding to the weights; learning the weights and the performance information to generate an objective function indicating a relationship between the weights and the performance information using a second machine learning model of a weight learning device; selecting a weight corresponding to a maximum performance using the objective function; and updating the first machine learning model with the weight.