Task offloading is a widely used technology in Mobile Edge Computing (MEC), which declines the completion time of user task with the help of resourceful edge servers. Existing works mainly focus on the case that the computation density of a user task is homogenous so that it can be offloaded in full or by percentage. However, various user tasks in real life consist of several inner dependent subtasks, each of which is a minimum execution unit logically. Motivated by this gap, we aim to solve the Dependent Task Offloading (DTO) problem under multi-user multi-edge scenario in this paper. We firstly use Directed Acyclic Graph (DAG) to represent dependent task where nodes indicate subtasks and directed edges indicate dependencies among subtasks. Then we propose a scheme based on Graph Attention Network (GAT) and Deep Reinforcement Learning (DRL) to minimize the makespan of user tasks. To utilize GAT efficiently, we put the training of it on resourceful cloud in unsupervised style due to the numerous data and computation resource requirements. In addition, we design a multi-discrete Action space for DRL algorithm to enhance the applicability of our proposed scheme. Experiments are conducted on broadly distributed synthetic data. The results demonstrate that our proposed approach can be adapted to both simple and complex MEC environments and outperforms other methods.

翻译：任务卸载是移动边缘计算（MEC）中广泛应用的技术，借助资源丰富的边缘服务器可降低用户任务的完成时间。现有研究主要关注用户任务计算密度同质化的场景，从而可进行整体或按比例卸载。然而，现实中的各类用户任务往往包含多个内部相互依赖的子任务，每个子任务在逻辑上均为最小执行单元。基于这一研究空白，本文旨在解决多用户多边缘场景下的依赖任务卸载（DTO）问题。首先，采用有向无环图（DAG）对依赖任务进行建模，其中节点表示子任务，有向边表示子任务间的依赖关系。进而提出一种基于图注意力网络（GAT）与深度强化学习（DRL）的方案，以最小化用户任务的总完成时间。为高效利用GAT，鉴于其对大量数据与计算资源的需求，我们在资源充足的云端以无监督方式完成其训练。此外，为增强所提方案的适用性，我们为DRL算法设计了多离散动作空间。实验在广泛分布的合成数据上进行，结果表明，本方法可适用于简单与复杂的MEC环境，且性能优于其他方法。