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：在快速发展的异构多接入边缘计算（HMEC）领域，高效的任务卸载对于优化系统吞吐量和资源利用率起着关键作用。然而，现有的任务卸载方法往往未能充分建模卸载任务间的依赖拓扑关系，这限制了其捕捉任务特征间复杂相互依赖关系的有效性。为克服这一局限，我们提出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征、网络状况及边缘侧可用资源等因素，并将这些捕获的特征嵌入图结构中。通过利用GNN，我们的机制能够捕捉并分析任务特征间的复杂关联，从而更全面地理解底层的依赖拓扑关系。在异构网络中的大量评估表明，我们提出的算法在优化系统吞吐量和资源利用率方面，相比贪婪算法和近似算法提升了18.6%至53.8%。实验结果表明，采用基于GNN的建模方法来考量任务特征间复杂相互作用具有显著优势。