View source for 18055788. RESOURCE PREDICTION FOR WORKLOADS simplified abstract (NVIDIA Corporation)

=RESOURCE PREDICTION FOR WORKLOADS=


==Organization Name==

[[:Category:NVIDIA Corporation|NVIDIA Corporation]]


[[Category:NVIDIA Corporation]]

==Inventor(s)==

[[:Category:Rohit Taneja of Fremont CA (US)|Rohit Taneja of Fremont CA (US)]][[Category:Rohit Taneja of Fremont CA (US)]]

[[:Category:Siddha Ganju of Santa Clara CA (US)|Siddha Ganju of Santa Clara CA (US)]][[Category:Siddha Ganju of Santa Clara CA (US)]]

[[:Category:Kash Krishna of San Jose CA (US)|Kash Krishna of San Jose CA (US)]][[Category:Kash Krishna of San Jose CA (US)]]

[[:Category:Brian Carpenter of Frisco TX (US)|Brian Carpenter of Frisco TX (US)]][[Category:Brian Carpenter of Frisco TX (US)]]

==RESOURCE PREDICTION FOR WORKLOADS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18055788 titled 'RESOURCE PREDICTION FOR WORKLOADS

==Simplified Explanation==

The patent application describes the use of neural networks to predict computing resources for workloads.

* Neural networks are utilized to forecast the computing resources needed for specific workloads.
* The technology aims to optimize resource allocation based on predictions made by the neural networks.

== Potential Applications ==
This technology could be applied in cloud computing environments to efficiently allocate resources based on workload predictions.

== Problems Solved ==
This technology addresses the challenge of resource allocation in computing systems by leveraging neural networks for accurate predictions.

== Benefits ==
The benefits of this technology include improved resource utilization, enhanced performance, and cost savings in computing environments.

== Potential Commercial Applications ==
"Optimizing Resource Allocation in Cloud Computing Environments Using Neural Networks"

== Possible Prior Art ==
Prior art may include research on resource allocation optimization in computing systems using machine learning techniques.

== Unanswered Questions ==
=== How does the accuracy of workload predictions compare to traditional resource allocation methods? ===
The article does not provide a comparison between the accuracy of workload predictions using neural networks versus traditional methods.

=== What are the potential limitations or drawbacks of using neural networks for resource prediction in computing environments? ===
The article does not discuss any potential limitations or drawbacks of implementing neural networks for resource prediction.


==Original Abstract Submitted==

Apparatuses, systems, and techniques to use one or more neural networks to predict one or more computing resources to perform one or more workloads are described.



[[Category:G06F9/50]]