Optimal Congestion-aware Routing and Offloading in Collaborative Edge Computing

Abstract

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, when considering network congestion, the optimal data/result routing and computation offloading strategy of CEC still remains an open problem. In this paper, we formulate a flow model of partial-offloading and multi-hop routing in CEC network with arbitrarily topology and heterogeneous communication/computation capability. 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. 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. Our proposed method allows asynchronous individual updating, and is adaptive to changes of network parameters. Numerical evaluation shows that our method significantly outperforms other baseline algorithms in multiple network instances, especially in congested scenarios.