Joint Task Offloading and Resource Allocation in Low-Altitude MEC via Graph Attention Diffusion

2025-06-27

AI Summary

To address challenges in low-altitude economy-oriented air-ground integrated multi-access edge computing (MEC) systems—including heterogeneous node coordination, unstable wireless links, and highly dynamic task workloads—this paper proposes a joint optimization framework for task offloading and resource allocation tailored to dynamic tasks. We construct a three-layer heterogeneous graph model that uniformly represents communication topologies, computational capabilities, and system constraints. We further introduce a novel graph attention diffusion mechanism, synergizing the contextual awareness of graph attention networks (GATs) with the diffusion model’s capacity to learn high-dimensional joint latent distributions, thereby enabling end-to-end co-optimization of discrete offloading decisions and continuous resource allocation. Evaluated on a multi-scale air-ground heterogeneous network simulator, our method achieves over 23% performance improvement over state-of-the-art baselines, while significantly enhancing robustness against link fluctuations and cross-task generalization capability—establishing a new paradigm for intelligent, high-dynamics scheduling in low-altitude airspace.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

With the rapid development of the low-altitude economy, air-ground integrated multi-access edge computing (MEC) systems are facing increasing demands for real-time and intelligent task scheduling. In such systems, task offloading and resource allocation encounter multiple challenges, including node heterogeneity, unstable communication links, and dynamic task variations. To address these issues, this paper constructs a three-layer heterogeneous MEC system architecture for low-altitude economic networks, encompassing aerial and ground users as well as edge servers. The system is systematically modeled from the perspectives of communication channels, computational costs, and constraint conditions, and the joint optimization problem of offloading decisions and resource allocation is uniformly abstracted into a graph-structured modeling task. On this basis, we propose a graph attention diffusion-based solution generator (GADSG). This method integrates the contextual awareness of graph attention networks with the solution distribution learning capability of diffusion models, enabling joint modeling and optimization of discrete offloading variables and continuous resource allocation variables within a high-dimensional latent space. We construct multiple simulation datasets with varying scales and topologies. Extensive experiments demonstrate that the proposed GADSG model significantly outperforms existing baseline methods in terms of optimization performance, robustness, and generalization across task structures, showing strong potential for efficient task scheduling in dynamic and complex low-altitude economic network environments.

Problem

• Optimize task offloading and resource allocation in low-altitude MEC systems
• Address node heterogeneity and unstable communication links
• Model joint optimization using graph attention diffusion

Innovation

• Three-layer heterogeneous MEC system architecture
• Graph attention diffusion-based solution generator
• Joint modeling of discrete and continuous variables