Machine Learning Model for Synthetic Storage Workload Generation
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Solution Overview
Problem
Embedded systems face inefficiencies due to unknown relationships between issued commands and storage component responses, leading to slow operations and excessive resource consumption.
Innovation Solution
A machine learning model is trained to generate synthetic storage transactions based on issued commands, optimizing the configuration of storage components by analyzing electrical signals and command patterns, thereby improving system performance and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional storage component configuration is used without machine learning optimization, then the system structure remains simple, but the operations are slow and resource consumption is excessive
Solution Approach 1:
The patent uses machine learning models to generate synthetic workload traces that copy and simulate real storage access patterns. These synthetic traces replicate the behavior of actual workloads without requiring physical deployment of complex real-world scenarios, enabling optimization through simulation rather than direct manipulation of complex systems
Solution Approach 2:
The patent optimizes storage component parameters (such as queue depths, buffer sizes, and access patterns) by training machine learning models on workload characteristics. The model learns optimal parameter configurations from training data and applies them to improve storage operation efficiency without manual system reconfiguration
2Reliability
If machine learning models are trained to generate synthetic workload traces, then storage component configuration can be optimized, but training data collection and model development add complexity
Solution Approach 1:
The system uses self-service principles by automatically collecting workload traces from the storage system itself and using these traces to train machine learning models. The storage system's own operational data is leveraged to create synthetic workloads that optimize its performance, eliminating the need for external data collection infrastructure
Solution Approach 2:
The patent implements feedback loops where machine learning models analyze storage access patterns, generate optimized configurations, and these configurations are tested by simulating workloads. The results feed back into model retraining, creating an iterative optimization process that continuously improves storage efficiency based on observed performance
3Use of energy by stationary object
If synthetic workload traces are generated using machine learning, then resource consumption is reduced compared to real workload simulation, but the accuracy of workload representation may be compromised
Solution Approach 1:
The patent replaces mechanical execution of actual storage workloads with machine learning-based synthetic generation. Instead of physically running workloads to collect data and simulate behavior, the system uses trained neural networks to directly generate accurate synthetic traces, substituting physical computation with intelligent data generation that consumes significantly less energy
Data Source
AI summary
Implementations described herein relate to using machine learning to generate a workload of a storage component. In some implementations, a device may obtain first data relating to commands issued by an operating system of a compute component of a computer system for a storage component of the computer system. The device may obtain second data relating to transactions at the storage component that are responsive to the commands. The device may provide the first data and the second data to train a machine learning model to output generated storage transactions based on an input of operating system commands.