18064909. INTELLIGENT CLOUD-EDGE RESOURCE MANAGEMENT simplified abstract (QUALCOMM Incorporated)
Contents
- 1 INTELLIGENT CLOUD-EDGE RESOURCE MANAGEMENT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INTELLIGENT CLOUD-EDGE RESOURCE MANAGEMENT - 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 Intelligent Cloud-Edge Resource Management
- 1.13 Original Abstract Submitted
INTELLIGENT CLOUD-EDGE RESOURCE MANAGEMENT
Organization Name
Inventor(s)
Mark Bapst of South Barrington IL (US)
Bibhu P. Mohanty of Del Mar CA (US)
Jay Rodney Walton of Waban MA (US)
INTELLIGENT CLOUD-EDGE RESOURCE MANAGEMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18064909 titled 'INTELLIGENT CLOUD-EDGE RESOURCE MANAGEMENT
Simplified Explanation
This patent application discusses a system for intelligent cloud-edge resource management, where end devices provide network link information to edge nodes to optimize task workload distribution.
- End devices share network link information with edge nodes for efficient task scheduling.
- Edge nodes can assign tasks to other nodes based on network link information.
- End devices can transmit task parameters and receive estimated completion times from edge nodes.
- Tasks can be assigned to nodes with the lowest completion time based on the information received.
Key Features and Innovation
- Intelligent cloud-edge resource management system.
- End devices provide network link information to edge nodes.
- Task workload distribution optimization based on network link information.
- Transmission of task parameters and estimated completion times between end devices and edge nodes.
Potential Applications
This technology can be applied in:
- Edge computing systems
- Internet of Things (IoT) networks
- Cloud computing environments
Problems Solved
- Inefficient task workload distribution in edge computing systems.
- Lack of network link information for optimal task scheduling.
- Uncertainty in task completion times in distributed computing environments.
Benefits
- Improved performance and efficiency in task distribution.
- Enhanced power management and security in edge computing systems.
- Increased network availability and reliability for mission-critical tasks.
Commercial Applications
Intelligent cloud-edge resource management technology can be utilized in various industries such as:
- Telecommunications
- Healthcare
- Manufacturing
Prior Art
Readers can explore prior research on edge computing, task scheduling algorithms, and network link optimization in distributed systems to understand the background of this technology.
Frequently Updated Research
Stay updated on advancements in edge computing, cloud-edge resource management, and network optimization techniques to enhance the implementation of this technology.
Questions about Intelligent Cloud-Edge Resource Management
How does this technology improve task distribution in edge computing systems?
This technology improves task distribution by providing network link information to edge nodes, enabling more efficient scheduling and workload distribution.
What are the potential benefits of using intelligent cloud-edge resource management in IoT networks?
Using this technology in IoT networks can lead to improved performance, power management, and security, enhancing the overall efficiency of the network.
Original Abstract Submitted
This disclosure provides systems, methods and apparatuses for intelligent cloud-edge resource management. An end device may provide edge nodes of an edge computing system with network link information, which may enable the edge nodes to schedule and distribute task workloads more effectively, providing greater performance, power, security, and mission-critical network availability. For example, if the end device transmits a processing task request to a first edge node, the first edge node may assign the processing task to a second edge node according to the network link information. Additionally, or alternatively, the end device may transmit an indication of processing task parameters to one or more edge nodes and may receive an indication of an estimated completion time of the processing task from the one or more edge nodes. Accordingly, the end device may assign the processing task to an edge node with the lowest completion time.
