17968936. RESOURCE INFRASTRUCTURE PREDICTION USING MACHINE LEARNING simplified abstract (Dell Products L.P.)
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 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 17968936 titled 'RESOURCE INFRASTRUCTURE PREDICTION USING MACHINE LEARNING
Simplified Explanation
The abstract describes a method for predicting the type and quantity of resources needed in a computing environment using a machine learning model trained on historical data.
- Machine learning model predicts type and quantity of resources for computing environment
- Model trained on historical resource data of users
Potential Applications
This technology could be applied in various industries such as cloud computing, data centers, and IT infrastructure management to optimize resource allocation and improve efficiency.
Problems Solved
1. Efficient resource allocation: By accurately predicting resource needs, organizations can avoid under or over-provisioning, leading to cost savings and improved performance. 2. Scalability: The ability to predict resource requirements can help businesses scale their operations effectively without encountering bottlenecks.
Benefits
1. Cost savings: Avoiding unnecessary resource allocation can result in significant cost savings for organizations. 2. Improved performance: By ensuring the right resources are available when needed, overall system performance can be optimized.
Potential Commercial Applications
Optimizing resource allocation in cloud computing services Enhancing capacity planning in data centers Improving IT infrastructure management for businesses
Possible Prior Art
One possible prior art could be predictive analytics tools used in resource management systems to forecast demand and optimize resource allocation.
Unanswered Questions
How does this method compare to traditional resource allocation strategies in terms of accuracy and efficiency?
This article does not provide a direct comparison between the proposed method and traditional resource allocation strategies. It would be interesting to see a study or analysis that evaluates the performance of this machine learning model against conventional methods.
What are the potential limitations or challenges in implementing this technology in real-world computing environments?
The article does not address any potential limitations or challenges that may arise when implementing this technology. It would be beneficial to explore factors such as data quality, model interpretability, and scalability issues that could impact the practical application of this method.
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.
