Dell products l.p. (20240135161). RESOURCE INFRASTRUCTURE PREDICTION USING MACHINE LEARNING simplified abstract
Contents
- 1 RESOURCE INFRASTRUCTURE PREDICTION USING MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 RESOURCE INFRASTRUCTURE PREDICTION USING MACHINE LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
RESOURCE INFRASTRUCTURE PREDICTION USING MACHINE LEARNING
Organization Name
Inventor(s)
Harish Mysore Jayaram of Cedar Park TX (US)
Bijan Kumar Mohanty of Austin TX (US)
Brent N. Davis of Phoenix AZ (US)
RESOURCE INFRASTRUCTURE PREDICTION USING MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135161 titled 'RESOURCE INFRASTRUCTURE PREDICTION USING MACHINE LEARNING
Simplified Explanation
The method described in the abstract involves using a machine learning model to predict the type and quantity of resources needed for a computing environment based on historical data.
- Machine learning model: The method utilizes a multiple output classification and regression machine learning model to make predictions.
- Dataset: The model is trained using a dataset that includes historical resource data from various users.
- Prediction: The model predicts the type and quantity of resources required in response to a request.
Potential Applications
This technology could be applied in various industries such as cloud computing, data centers, and IT infrastructure management.
Problems Solved
This technology helps in optimizing resource allocation and planning in a computing environment, leading to improved efficiency and cost-effectiveness.
Benefits
The benefits of this technology include better resource utilization, reduced wastage, and improved overall performance of the computing environment.
Potential Commercial Applications
One potential commercial application of this technology could be in cloud service providers to optimize resource allocation for their customers.
Possible Prior Art
Prior art in this field may include similar machine learning models used for resource prediction in different contexts, such as network traffic prediction or server load forecasting.
Unanswered Questions
How does the model handle dynamic changes in resource requirements over time?
The abstract does not provide information on how the machine learning model adapts to changing resource needs.
What is the accuracy of the predictions made by the model?
The abstract does not mention the accuracy metrics or validation methods used to evaluate the performance of the machine learning model.
Original Abstract Submitted
a method comprises receiving a request to predict a type and a quantity of respective ones of a plurality of resources for a computing environment. using a multiple output classification and regression machine learning model, the type and the quantity of the respective ones of the plurality of resources are predicted in response to the request. the machine learning model is trained with a dataset comprising historical resource data corresponding to respective ones of a plurality of users.
