18596108. USING MACHINE LEARNING TO GENERATE A WORKLOAD OF A STORAGE COMPONENT simplified abstract (Micron Technology, Inc.)
Contents
- 1 USING MACHINE LEARNING TO GENERATE A WORKLOAD OF A STORAGE COMPONENT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 USING MACHINE LEARNING TO GENERATE A WORKLOAD OF A STORAGE COMPONENT - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Machine Learning in Storage Workload Optimization
- 1.13 Original Abstract Submitted
USING MACHINE LEARNING TO GENERATE A WORKLOAD OF A STORAGE COMPONENT
Organization Name
Inventor(s)
Saideep Tiku of Folsom CA (US)
USING MACHINE LEARNING TO GENERATE A WORKLOAD OF A STORAGE COMPONENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18596108 titled 'USING MACHINE LEARNING TO GENERATE A WORKLOAD OF A STORAGE COMPONENT
Simplified Explanation
This patent application describes using machine learning to generate a workload for a storage component based on commands issued by an operating system.
Key Features and Innovation
- Device obtains data on commands from operating system for storage component
- Device collects data on transactions at storage component in response to commands
- Machine learning model trained to output generated storage transactions based on operating system commands
Potential Applications
This technology could be used in data centers to optimize storage performance based on workload predictions generated by machine learning.
Problems Solved
This technology addresses the challenge of efficiently managing storage workloads in computer systems by using machine learning to predict and generate storage transactions.
Benefits
- Improved storage performance
- Enhanced workload management
- Increased efficiency in data centers
Commercial Applications
Predictive Storage Workload Optimization in Data Centers
This technology can be commercially applied in data centers to optimize storage performance based on workload predictions generated by machine learning models.
Prior Art
Readers can explore prior art related to machine learning in storage workload optimization to gain a deeper understanding of the technological advancements in this field.
Frequently Updated Research
Researchers are constantly exploring new methods and algorithms in machine learning for storage workload optimization, leading to ongoing advancements in this technology.
Questions about Machine Learning in Storage Workload Optimization
How does machine learning improve storage performance in data centers?
Machine learning helps predict storage workloads and generate optimized storage transactions, leading to improved performance in data centers.
What are the key challenges in implementing machine learning for storage workload optimization?
One of the key challenges is ensuring the accuracy and efficiency of the machine learning models in predicting storage workloads and generating optimized transactions.
Original Abstract Submitted
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.
