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

TL;DR

This paper introduces a graph attention diffusion approach for joint task offloading and resource allocation in low-altitude MEC systems, effectively handling heterogeneity and dynamic conditions to improve efficiency and robustness.

Contribution

It develops a novel graph attention diffusion-based method that models and optimizes offloading and resource allocation jointly in a complex MEC environment.

Findings

Significantly outperforms baseline methods in simulations

Demonstrates robustness and generalization across various task structures

Effective in dynamic and complex low-altitude network environments

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.