Emerging edge computing paradigms enable heterogeneous devices to collaborate on complex computation applications. However, for congestible links and computing units, delay-optimal forwarding and offloading for service chain tasks (e.g., DNN with vertical split) in edge computing networks remains an open problem. In this paper, we formulate the service chain forwarding and offloading problem with arbitrary topology and heterogeneous transmission/computation capability, and aim to minimize the aggregated network cost. We consider congestion-aware nonlinear cost functions that cover various performance metrics and constraints, such as average queueing delay with limited processor capacity. We solve the non-convex optimization problem globally by analyzing the KKT condition and proposing a sufficient condition for optimality. We then propose a distributed algorithm that converges to the global optimum. The algorithm adapts to changes in input rates and network topology, and can be implemented as an online algorithm. Numerical evaluation shows that our method significantly outperforms baselines in multiple network instances, especially in congested scenarios.

翻译：新兴的边缘计算范式使得异构设备能够协作完成复杂的计算应用。然而，对于存在拥塞的链路和计算单元，边缘计算网络中服务链任务（如垂直分割的深度神经网络）的时延最优转发与卸载仍是一个开放性问题。本文针对任意拓扑及异构传输/计算能力场景，构建了服务链转发与卸载问题模型，旨在最小化聚合网络成本。我们考虑了感知拥塞的非线性成本函数，该函数涵盖多种性能指标与约束条件，例如有限处理器容量下的平均排队时延。通过分析KKT条件并提出最优性的充分条件，我们实现了对非凸优化问题的全局求解。随后提出一种收敛至全局最优的分布式算法，该算法能自适应输入速率与网络拓扑的变化，并可作为在线算法实现。数值评估表明，在多个网络实例中（尤其是拥塞场景），本方法显著优于基线方案。