Nvidia corporation (20240160491). RESOURCE PREDICTION FOR WORKLOADS simplified abstract
Contents
- 1 RESOURCE PREDICTION FOR WORKLOADS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 RESOURCE PREDICTION FOR WORKLOADS - 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 PREDICTION FOR WORKLOADS
Organization Name
Inventor(s)
Rohit Taneja of Fremont CA (US)
Siddha Ganju of Santa Clara CA (US)
Kash Krishna of San Jose CA (US)
Brian Carpenter of Frisco TX (US)
RESOURCE PREDICTION FOR WORKLOADS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240160491 titled 'RESOURCE PREDICTION FOR WORKLOADS
Simplified Explanation
The patent application describes apparatuses, systems, and techniques that utilize neural networks to predict computing resources for workloads.
- Neural networks are used to predict computing resources for workloads.
- The technology involves the use of one or more neural networks.
- The prediction is made based on the workload requirements.
- This innovation aims to optimize resource allocation for efficient performance.
Potential Applications
This technology could be applied in cloud computing environments to optimize resource allocation for various workloads.
Problems Solved
This technology addresses the challenge of efficiently allocating computing resources to different workloads based on their requirements.
Benefits
The use of neural networks for resource prediction can lead to improved performance and cost-effectiveness in computing environments.
Potential Commercial Applications
"Optimizing Resource Allocation in Cloud Computing Environments"
Possible Prior Art
There may be prior art related to resource allocation in computing environments, but specific examples are not provided in this context.
Unanswered Questions
How does this technology handle dynamic workload changes?
The patent abstract does not specify how the neural networks adapt to changing workload requirements.
What types of workloads are best suited for this resource prediction technology?
The abstract does not mention specific types of workloads that would benefit most from this innovation.
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.
