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：协作边缘计算（CEC）是一种新兴范式，旨在使异构设备协同完成边缘计算任务。针对可拥塞链路与计算单元，如何在CEC中实现服务链任务（例如采用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未解决的问题。本文针对任意拓扑结构与异构传输/计算能力的CEC场景，对服务链的转发与卸载问题进行建模，旨在最小化网络聚合代价。我们考虑拥塞感知的非线性代价函数，其覆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延迟）。通过分析KKT条件并推导充分最优性条件，我们实现了对非凸优化问题的全局求解。我们提出了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，并能适应输入速率与网络拓扑的变化，可作为在线算法实现。数值评估表明，在多种网络实例中，尤其是在拥塞场景下，所提方法显著优于基线方案。