Collaborative edge computing (CEC) is an emerging paradigm for heterogeneous devices to collaborate on edge computation jobs. For congestible links and computing units, delay-optimal forwarding and offloading for service chain tasks (e.g., DNN with vertical split) in CEC remains an open problem. In this paper, we formulate the service chain forwarding and offloading in CEC with arbitrary topology and heterogeneous transmission/computation capability, and aim to minimize the network aggregated 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 sufficiency optimality condition. We propose a polynomial-time 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：协同边缘计算（CEC）是一种异构设备协作处理边缘计算作业的新兴范式。针对可拥塞链路和计算单元，在CEC中实现服务链任务（如垂直分割的深度神经网络）的延迟最优转发与卸载仍是一个开放性问题。本文对任意拓扑结构和异构传输/计算能力场景下的CEC服务链转发与卸载问题进行建模，旨在最小化网络聚合代价。我们考虑包含拥塞感知的非线性代价函数，该函数覆盖了平均排队时延、处理器容量约束等多种性能指标与约束条件。通过分析KKT条件并提出充分最优性条件，实现了非凸优化问题的全局求解。我们提出一种多项式时间复杂度的分布式算法，该算法可收敛至全局最优解。该算法能自适应输入速率和网络拓扑的变化，并可作为在线算法实现。数值评估表明，在多个网络实例中，本方法显著优于基线方案，在拥塞场景下尤为突出。