Collaborative edge computing (CEC) is an emerging paradigm where heterogeneous edge devices collaborate to fulfill computation tasks, such as model training or video processing, by sharing communication and computation resources. Nevertheless, the optimal data/result routing and computation offloading strategy in CEC with arbitrary topology still remains an open problem. In this paper, we formulate the flow model of partial-offloading and multi-hop routing for arbitrarily divisible tasks, where each node individually decides its routing/offloading strategy. In contrast to most existing works, our model applies to tasks with non-negligible result size, and allows data sources to be distinct from the result destination. We propose a network-wide cost minimization problem with congestion-aware convex cost functions for communication and computation. Such convex cost covers various performance metrics and constraints, such as average queueing delay with limited processor capacity. Although the problem is non-convex, we provide necessary conditions and sufficient conditions for the global-optimal solution, and devise a fully distributed algorithm that converges to the optimum in polynomial time, allows asynchronous individual updating, and is adaptive to changes in task pattern. Numerical evaluation shows that our proposed method significantly outperforms other baseline algorithms in multiple network instances, especially in congested scenarios.

翻译：合作边缘计算(CEC)是一个新兴范例,不同边缘装置通过共享通信和计算资源,合作完成模型培训或视频处理等计算任务。然而,在CEC中,最佳数据/结果路由和计算卸载战略与任意的地形学仍然是一个尚未解决的问题。在本文件中,我们为任意分散的任务制定了部分卸载和多希望路由流模式,每个节点单独决定其路由/卸载战略。与大多数现有工程不同,我们的模式适用于非忽略性结果大小的任务,使数据源与结果目的地区别开来。我们提出了一个全网络成本最小化问题,即通信和计算费用功能为拥拥堵-觉的松动成本功能。在本文中,这种convex成本涵盖各种性指标和制约因素,如流程能力有限的平均排队延迟。尽管问题不是相互趋同,但我们为全球最佳解决方案提供了必要的条件和充分条件,并设计一个完全分布的算法,与多元时的最佳结果尺寸相匹配,使数据源与结果源与结果不同。我们提出了一个全网范围成本最小化的最小化问题,因此可以大幅更新和计算方法,特别是调整了我们的网络模式。