Storage System Simulation Model for Workload-Based QoS Tuning
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Solution Overview
Problem
Optimizing Quality of Service (QoS) in storage devices is challenging due to the high-dimensional parameter space and conflicting parameters, leading to time-consuming and unpredictable results, with existing methods relying on trial-and-error and intuition.
Innovation Solution
A storage system utilizing a machine learning-based simulation model to train and optimize parameters and workloads, calculating a loss between predicted and real QoS data to enhance QoS by selecting optimized parameters and workloads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional trial-and-error methods are used to optimize QoS parameters, then optimization can be performed with simple tools, but the optimization time becomes excessively long and results are unpredictable
Solution Approach 1:
The patent creates a simulation model that copies the behavior of the storage device across multiple layers (HIL, FTL, FIL). This virtual copy allows rapid experimentation and optimization without affecting the actual device, enabling quick iteration through parameter adjustments while maintaining realistic QoS performance characteristics.
Solution Approach 2:
The simulation model is trained in advance using historical QoS data to learn the complex relationships between parameters and performance metrics. This preliminary training phase prepares the model to quickly predict optimal parameters for new workloads without requiring time-consuming real-world experimentation.
2Ease of operation
If QoS parameters are tuned using developer intuition and experience, then the process is simple to initiate, but the determined parameters are likely to be local optima rather than global optima
Solution Approach 1:
The system implements a feedback mechanism where the simulation model continuously learns from the discrepancy between predicted QoS data and actual QoS data. Through loss calculation and model updates, the system refines its parameter recommendations iteratively, ensuring that optimized parameters achieve global optimality rather than getting trapped in local optima.
Solution Approach 2:
The patent systematically explores the high-dimensional parameter space by making controlled changes to multiple parameters simultaneously across different layers. The simulation model evaluates the combined effect of these parameter changes on QoS metrics, enabling discovery of optimal parameter combinations that individual intuition cannot identify.
3Reliability
If comprehensive QoS optimization is attempted across multiple layers (HIL, FTL, FIL), then QoS performance can be improved, but the complexity of the optimization process increases significantly
Solution Approach 1:
The patent merges the optimization processes of multiple layers (HIL, FTL, FIL) into a unified simulation model. This integrated model captures the interactions between layers and allows simultaneous optimization of parameters across all layers, reducing the overall complexity compared to separate optimization processes while improving comprehensive QoS performance.
4Reliability
If tuning is performed after QoS algorithms are completed, then the storage device meets initial requirements, but the tuning process requires significant time and cost
Solution Approach 1:
The simulation model is trained in advance using historical QoS data to learn the complex relationships between parameters and performance metrics. This preliminary training phase prepares the model to quickly predict optimal parameters for new workloads without requiring time-consuming real-world experimentation.
Data Source
AI summary
A storage system may be connected to a machine learning model embedded device that includes a trained simulation model. The simulation model may be trained based on a loss function, where the loss function is based on a comparison of predicted QoS data and real QOS data. The simulation model may be trained to search for a workload for increasing the QoS using a fixed parameter or, conversely, to search for a parameter for maximizing the QoS using a fixed workload. Further, the machine learning model embedded device may include a map table storing the optimized parameters calculated by the trained simulation model so as to maximize QoS relative to each workload.