Dell products l.p. (20240305535). SYSTEMS AND METHODS FOR EDGE SYSTEM RESOURCE CAPACITY DYNAMIC POLICY PLANNING FRAMEWORK simplified abstract
Contents
- 1 SYSTEMS AND METHODS FOR EDGE SYSTEM RESOURCE CAPACITY DYNAMIC POLICY PLANNING FRAMEWORK
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR EDGE SYSTEM RESOURCE CAPACITY DYNAMIC POLICY PLANNING FRAMEWORK - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Edge Computing
- 1.13 Original Abstract Submitted
SYSTEMS AND METHODS FOR EDGE SYSTEM RESOURCE CAPACITY DYNAMIC POLICY PLANNING FRAMEWORK
Organization Name
Inventor(s)
William Jeffery White of Plano TX (US)
SYSTEMS AND METHODS FOR EDGE SYSTEM RESOURCE CAPACITY DYNAMIC POLICY PLANNING FRAMEWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240305535 titled 'SYSTEMS AND METHODS FOR EDGE SYSTEM RESOURCE CAPACITY DYNAMIC POLICY PLANNING FRAMEWORK
Simplified Explanation
Managing the resource demand load for edge systems is more complex than for cloud environments due to the unique characteristics of edge data.
- Edge systems have complex waveforms, Pareto/alpha-stable distributions, and long-range dependence.
- Elaborately designed embodiments can estimate scaling and multi-fractal dimensionality to create predictive models.
Key Features and Innovation
- Edge systems present complex waveforms and distributions, unlike cloud systems.
- Elaborately designed embodiments can estimate scaling and multi-fractal dimensionality for predictive models.
Potential Applications
This technology can be applied in various industries such as telecommunications, IoT, and smart cities to optimize resource management in edge systems.
Problems Solved
This technology addresses the challenges of managing resource demand load in edge systems with complex data characteristics.
Benefits
- Improved resource management in edge systems.
- Enhanced predictive modeling for better decision-making.
- Increased efficiency and performance in edge computing environments.
Commercial Applications
Optimizing resource management in edge systems can lead to cost savings, improved performance, and enhanced user experiences in various industries.
Prior Art
Readers can explore prior research on edge computing, resource management, and predictive modeling to understand the background of this technology.
Frequently Updated Research
Stay updated on the latest advancements in edge computing, resource management, and predictive modeling to enhance the application of this technology.
Questions about Edge Computing
How does edge computing differ from cloud computing?
Edge computing processes data closer to the source, reducing latency and bandwidth usage compared to cloud computing, which centralizes data processing in remote servers.
What are the key challenges in managing resource demand load in edge systems?
The unique characteristics of edge data, such as complex waveforms and long-range dependence, pose challenges in accurately predicting resource demand load in edge systems.
Original Abstract Submitted
managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. unlike cloud systems and other frameworks that are able to use closed-form solutions based on poisson processes or other tractable gaussian-based probability distributions, edge systems present complex waveforms, pareto/alpha-stable distributions, and long-range dependence. based on elaborately designed embodiments that recognize the complexities of edge data, one can estimate scaling and multi-fractal dimensionality to determine predictive models.
