Fair Task Offloading in Edge Service Networks
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Solution Overview
Problem
In edge computing networks, existing methods for load balancing and task offloading fail to ensure fair resource allocation across users, leading to suboptimal utility and service quality due to limited resources and conflicting demands, often sacrificing individual user performance for overall efficiency.
Innovation Solution
A fair task offloading and migration method that uses a fair utility evaluation function and graph neural networks to optimize resource allocation, incorporating personalized utility weights and reinforcement learning to determine optimal migration paths, ensuring Pareto optimality and maximizing overall user utility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing load balancing methods focus on overall task efficiency and network bandwidth utilization, then overall system performance is improved, but individual user utility and service quality deteriorate due to unfair resource allocation
Solution Approach 1:
The patent transforms the resource allocation problem from a single-objective optimization to a multi-objective optimization by changing the parameter space to include both overall efficiency metrics and individual user utility metrics. The fair utility evaluation function evaluates multiple parameters simultaneously (task completion rate, user utility, resource utilization) to achieve balanced optimization.
Solution Approach 2:
The patent segments the user base into different categories based on task types (bandwidth-sensitive tasks vs. computing-sensitive tasks) and applies differentiated resource allocation strategies for each segment. This allows the system to optimize for overall efficiency while ensuring fair treatment of different user groups with different service quality requirements.
2Quantity of substance
If resources are allocated to maximize overall system utility, then total resource utilization is improved, but individual user fairness deteriorates due to resource allocation conflicts among multiple users
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors both overall resource utilization metrics and individual user utility metrics. The fair utility evaluation function uses this feedback to dynamically adjust resource allocation decisions, ensuring that maximizing total resource utilization does not come at the expense of individual user fairness. The reinforcement learning component learns from historical allocation outcomes to improve future fairness-utility tradeoffs.
3Manufacturing precision
If task migration is used to execute user tasks on appropriate servers, then task completion accuracy is improved, but system complexity increases due to path selection and resource coordination requirements
Solution Approach 1:
The patent introduces a centralized task scheduling system that acts as an intermediary between users and edge servers. This intermediary collects task requirements, evaluates feasible migration paths, and makes centralized allocation decisions. The fair utility evaluation function serves as the decision-making intermediary that balances task completion accuracy requirements with system complexity constraints by selecting optimal migration paths based on multiple criteria including user utility, resource availability, and path feasibility.
4Adaptability or versatility
If personalized resource allocation is implemented to meet individual user needs, then user utility is improved, but resource allocation conflict increases among multiple users with competing demands
Solution Approach 1:
The patent resolves resource allocation conflicts by changing the optimization parameters from individual user-centric metrics to a combined multi-objective function. The fair utility evaluation function transforms personalized resource allocation into a coordinated multi-user optimization problem, where the system adjusts allocation parameters to simultaneously satisfy individual user needs while minimizing overall system conflict through fair utility maximization.
Data Source
AI summary
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.