In the rapidly evolving field of Heterogeneous Multi-access Edge Computing (HMEC), efficient task offloading plays a pivotal role in optimizing system throughput and resource utilization. However, existing task offloading methods often fall short of adequately modeling the dependency topology relationships between offloaded tasks, which limits their effectiveness in capturing the complex interdependencies of task features. To address this limitation, we propose a task offloading mechanism based on Graph Neural Networks (GNN). Our modeling approach takes into account factors such as task characteristics, network conditions, and available resources at the edge, and embeds these captured features into the graph structure. By utilizing GNNs, our mechanism can capture and analyze the intricate relationships between task features, enabling a more comprehensive understanding of the underlying dependency topology. Through extensive evaluations in heterogeneous networks, our proposed algorithm improves 18.6\%-53.8\% over greedy and approximate algorithms in optimizing system throughput and resource utilization. Our experiments showcase the advantage of considering the intricate interplay of task features using GNN-based modeling.

翻译：在异构多接入边缘计算（HMEC）这一快速发展的领域中，高效的任务卸载在优化系统吞吐量和资源利用率方面发挥着关键作用。然而，现有的任务卸载方法往往未能充分建模卸载任务之间的依赖拓扑关系，这限制了其在捕捉任务特征复杂相互依赖性方面的有效性。为解决这一局限，我们提出了一种基于图神经网络（GNN）的任务卸载机制。我们的建模方法综合考虑了任务特性、网络条件以及边缘可用资源等因素，并将这些捕获的特征嵌入到图结构中。通过利用GNN，我们的机制能够捕捉并分析任务特征之间的复杂关系，从而更全面地理解底层依赖拓扑。在异构网络中的广泛评估表明，与贪婪算法和近似算法相比，我们提出的算法在优化系统吞吐量和资源利用率上提升了18.6%至53.8%。我们的实验证明了利用基于GNN的建模考虑任务特征复杂交互作用的优势。