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