Dell products l.p. (20240232605). RESOURCE INFRASTRUCTURE PREDICTION USING MACHINE LEARNING simplified abstract
Contents
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 20240232605 titled 'RESOURCE INFRASTRUCTURE PREDICTION USING MACHINE LEARNING
The method described in the abstract involves predicting the type and quantity of resources for a computing environment using a machine learning model trained with historical resource data.
- The method receives a request to predict the type and quantity of resources for a computing environment.
- It utilizes a multiple output classification and regression machine learning model to make predictions.
- The machine learning model is trained with a dataset containing historical resource data from multiple users.
Potential Applications: - Resource allocation in cloud computing environments - Capacity planning for IT infrastructure - Predictive maintenance for hardware components
Problems Solved: - Efficient resource utilization - Cost optimization in resource provisioning - Improved performance through accurate resource allocation
Benefits: - Enhanced scalability and flexibility in resource management - Cost savings through optimized resource allocation - Improved user experience with better performance
Commercial Applications: Predictive resource allocation technology can be used by cloud service providers, IT departments, and data centers to optimize resource usage, reduce costs, and improve overall system performance.
Questions about Predictive Resource Allocation: 1. How does predictive resource allocation benefit cloud computing environments? Predictive resource allocation helps cloud providers optimize resource usage, reduce costs, and improve performance by accurately predicting resource needs. 2. What are the key advantages of using a machine learning model for predicting resource allocation? Machine learning models can analyze large datasets and identify patterns to make accurate predictions, leading to more efficient resource allocation strategies.
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.
