ZHEJIANG UNIVERSITY ZHONGYUAN INSTITUTE (20240264883). A FAIR TASK OFFLOADING AND MIGRATION METHOD FOR EDGE SERVICE NETWORKS simplified abstract

A FAIR TASK OFFLOADING AND MIGRATION METHOD FOR EDGE SERVICE NETWORKS

Organization Name

ZHEJIANG UNIVERSITY ZHONGYUAN INSTITUTE

Inventor(s)

SHUIGUANG Deng of HANGZHOU, ZHEJIANG PROVINCE (CN)

CHENG Zhang of HANGZHOU, ZHEJIANG PROVINCE (CN)

JIANWEI Yin of HANGZHOU, ZHEJIANG PROVINCE (CN)

A FAIR TASK OFFLOADING AND MIGRATION METHOD FOR EDGE SERVICE NETWORKS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240264883 titled 'A FAIR TASK OFFLOADING AND MIGRATION METHOD FOR EDGE SERVICE NETWORKS

Simplified Explanation: The patent application describes a method for fair task offloading and migration in edge service networks, optimizing the utility function of user tasks while considering network resource constraints.

• Uses Pareto optimality to maximize utility function of all user tasks in the system
• Introduces a new quantitative measurement index for task utility quality improvement
• Utilizes graph neural network and reinforcement learning algorithm for optimization
• Efficiently achieves Pareto optimal results in multi-user complex tasks

Key Features and Innovation: - Optimization of utility function for user tasks in edge service networks - Consideration of network resource constraints - Introduction of a new measurement index for task utility quality - Utilization of graph neural network and reinforcement learning algorithm for optimization - Efficient achievement of Pareto optimal results in multi-user scenarios

Potential Applications: - Edge computing networks - Multi-user task environments - Resource-constrained networks

Problems Solved: - Efficient task offloading and migration in edge service networks - Maximizing utility function of user tasks - Addressing network resource constraints - Improving task utility quality under multi-user competition

Benefits: - Enhanced service quality in edge network environments - Improved user experience - Efficient allocation of network resources - Maximization of utility function for all user tasks

Commercial Applications: Title: Fair Task Offloading and Migration Method for Edge Service Networks This technology can be applied in various industries such as telecommunications, IoT, cloud computing, and edge computing services. It can improve the efficiency of task allocation in network systems, leading to enhanced user experiences and optimized resource utilization.

Prior Art: Prior art related to this technology may include research papers, patents, and academic studies on task offloading, edge computing, and network optimization algorithms.

Frequently Updated Research: Researchers are constantly exploring new algorithms and techniques to optimize task offloading and migration in edge service networks. Stay updated on advancements in graph neural networks, reinforcement learning, and Pareto optimality in multi-user scenarios.

Questions about Fair Task Offloading and Migration Method for Edge Service Networks: 1. How does the proposed method optimize the utility function of user tasks in edge service networks? 2. What are the key benefits of using graph neural networks and reinforcement learning algorithms in this context?

Original Abstract Submitted

the present invention discloses a fair task offloading and migration method for edge service networks, taking the pareto optimality of the utility function of all user tasks executed by the edge system as the optimization objective, this approach not only takes into account the constraints of edge network resources, but also ensures the maximization of the utility function of all user tasks in the system, it proposes a new quantitative measurement index for improving the task utility quality under multi-user competition. in addition, the present invention uses the graph neural network and reinforcement learning algorithm to solve the final optimization goal, this algorithm has high execution efficiency and returns accurate approximate results, which is particularly suitable for the scene of edge network system under multi-user complex tasks, so that when multi-user tasks compete for network resources, the edge computing network system can efficiently obtain the pareto optimal result of multi-user utility function, greatly improving the service quality and user experience of edge network environments.