Nvidia corporation (20240129380). DATA CENTER JOB SCHEDULING USING MACHINE LEARNING simplified abstract
Contents
- 1 DATA CENTER JOB SCHEDULING USING MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DATA CENTER JOB SCHEDULING 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
DATA CENTER JOB SCHEDULING USING MACHINE LEARNING
Organization Name
Inventor(s)
Siddha Ganju of Santa Clara CA (US)
Elad Mentovich of Tel Aviv (IL)
Michael Balint of Nashville TN (US)
Eitan Zahavi of Zichron Yaakov (IL)
Michael Sabotta of Cypress TX (US)
Michael Norman of Redwood City CA (US)
DATA CENTER JOB SCHEDULING USING MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240129380 titled 'DATA CENTER JOB SCHEDULING USING MACHINE LEARNING
Simplified Explanation
The method described in the abstract involves using machine learning and reinforcement learning techniques to optimize the location of an operation at a data center based on certain conditions. Here is a simplified explanation of the abstract:
- Receiving and processing a condition associated with an operation at a data center
- Providing the condition as input to a machine learning model
- Using reinforcement learning techniques to determine a final location for the operation that is closer to a target
Potential Applications
The technology described in this patent application could be applied in various industries where optimizing the location of operations is crucial, such as logistics, supply chain management, and data center management.
Problems Solved
This technology solves the problem of efficiently determining the best location for an operation at a data center based on certain conditions, ultimately improving overall performance and efficiency.
Benefits
The benefits of this technology include increased efficiency, optimized resource allocation, and improved decision-making processes in data center operations.
Potential Commercial Applications
One potential commercial application of this technology could be in cloud computing services, where optimizing the location of operations within data centers can lead to improved service delivery and cost-effectiveness.
Possible Prior Art
One possible prior art for this technology could be existing machine learning and reinforcement learning techniques used in various industries for optimization and decision-making processes.
Unanswered Questions
How does this technology handle real-time data updates and changes in conditions at the data center?
This article does not address how the technology adapts to real-time changes in conditions at the data center and whether it can dynamically adjust the location of operations based on new information.
What are the potential limitations or challenges of implementing this technology in a practical setting?
The article does not discuss any potential limitations or challenges that may arise when implementing this technology in real-world data center operations, such as scalability issues or data privacy concerns.
Original Abstract Submitted
a method includes receiving, using a processing device, a first condition associated with an operation at a data center, where the operation at the data center pertains to a first location at the data center, the first location corresponding to a first parameter value. the method further includes providing the first condition as an input to a machine learning model. the method also includes performing one or more reinforcement learning techniques using the machine learning model to cause the machine learning model to output an indication of a final location associated with the operation, where the final location corresponds to a final parameter value that is closer to a target than the first parameter value corresponding to the first location at the data center.
