Dell products l.p. (20240135229). MOVEMENT OF OPERATIONS BETWEEN CLOUD AND EDGE PLATFORMS simplified abstract
Contents
- 1 MOVEMENT OF OPERATIONS BETWEEN CLOUD AND EDGE PLATFORMS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MOVEMENT OF OPERATIONS BETWEEN CLOUD AND EDGE PLATFORMS - 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
MOVEMENT OF OPERATIONS BETWEEN CLOUD AND EDGE PLATFORMS
Organization Name
Inventor(s)
Subhasis Bandyopadhyay of Bangalore (IN)
Parminder Singh Sethi of Ludhiana (IN)
MOVEMENT OF OPERATIONS BETWEEN CLOUD AND EDGE PLATFORMS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135229 titled 'MOVEMENT OF OPERATIONS BETWEEN CLOUD AND EDGE PLATFORMS
Simplified Explanation
The abstract describes techniques for transferring machine learning operations between cloud and edge platforms based on analysis of results.
- Machine learning algorithm executed on cloud platform
- Results analyzed to determine if additional training is needed
- If no further training needed, algorithm execution transferred to edge platform
Potential Applications
This technology can be applied in various fields such as:
- Internet of Things (IoT) devices
- Autonomous vehicles
- Real-time data analysis
Problems Solved
This technology addresses the following issues:
- Efficient resource utilization
- Reduced latency for real-time processing
- Seamless transition between cloud and edge computing environments
Benefits
The benefits of this technology include:
- Improved performance of machine learning algorithms
- Cost-effective utilization of cloud and edge resources
- Enhanced scalability and flexibility in computing tasks
Potential Commercial Applications
This technology has potential commercial applications in:
- Telecommunications industry
- Healthcare sector
- Manufacturing and industrial automation
Possible Prior Art
One possible prior art for this technology could be the concept of distributed computing systems that involve transferring computing tasks between different platforms based on specific criteria.
What are the security implications of transferring machine learning operations between cloud and edge platforms?
Transferring machine learning operations between cloud and edge platforms may raise security concerns related to data privacy, network vulnerabilities, and unauthorized access to sensitive information. Implementing robust encryption protocols and access control mechanisms can help mitigate these security risks.
How does the performance of machine learning algorithms differ when executed on cloud versus edge platforms?
The performance of machine learning algorithms may vary based on factors such as network latency, processing power, and data volume. Cloud platforms typically offer higher computational capabilities but may introduce latency issues, while edge platforms provide real-time processing but with limited resources. Optimizing algorithms for specific platforms can help achieve the desired performance outcomes.
Original Abstract Submitted
techniques are disclosed for moving operations between cloud and edge platforms. for example, a method comprises executing a machine learning algorithm on a cloud platform and analyzing results of executing the machine learning algorithm. based at least in part on the analysis, a determination is made whether the machine learning algorithm should be additionally trained. based at least in part on a negative determination further execution of the machine learning algorithm is transferred from the cloud platform to an edge platform.
