Collaborative Edge Computing (CEC) is an emerging paradigm that collaborates heterogeneous edge devices as a resource pool to compute DNN inference tasks in proximity such as edge video analytics. Nevertheless, as the key knob to improve network utility in CEC, existing works mainly focus on the workload routing strategies among edge devices with the aim of minimizing the routing cost, remaining an open question for joint workload allocation and routing optimization problem from a system perspective. To this end, this paper presents a holistic, learned optimization for CEC towards maximizing the total network utility in an online manner, even though the utility functions of task input rates are unknown a priori. In particular, we characterize the CEC system in a flow model and formulate an online learning problem in a form of cross-layer optimization. We propose a nested-loop algorithm to solve workload allocation and distributed routing iteratively, using the tools of gradient sampling and online mirror descent. To improve the convergence rate over the nested-loop version, we further devise a single-loop algorithm. Rigorous analysis is provided to show its inherent convexity, efficient convergence, as well as algorithmic optimality. Finally, extensive numerical simulations demonstrate the superior performance of our solutions.

翻译：协作边缘计算（CEC）是一种新兴范式，它将异构边缘设备协作组织为资源池，以就近计算DNN推理任务（如边缘视频分析）。然而，作为提升CEC网络效用的关键调控手段，现有工作主要聚焦于边缘设备间的工作负载路由策略，旨在最小化路由成本，从系统视角出发的联合工作负载分配与路由优化问题仍悬而未决。为此，本文提出了一种面向CEC的整体性学习优化方法，旨在以在线方式最大化总网络效用，即使任务输入速率的效用函数先验未知。具体而言，我们将CEC系统建模为流模型，并以跨层优化形式构建在线学习问题。我们提出一种嵌套循环算法，利用梯度采样和在线镜像下降工具，迭代求解工作负载分配与分布式路由问题。为提升嵌套循环版本的收敛速度，我们进一步设计了一种单循环算法。严格的理论分析证明了其内在凸性、高效收敛性及算法最优性。最后，大量数值仿真验证了我们方案的优越性能。