Result and Congestion Aware Optimal Routing and Partial Offloading in Collaborative Edge Computing

TL;DR

This paper introduces a novel congestion-aware routing and partial offloading strategy for collaborative edge computing, optimizing data flow and computation to reduce delays and improve performance in arbitrary network topologies.

Contribution

It formulates a new partial-offloading and multi-hop routing model for divisible tasks with non-negligible result sizes, and proposes a distributed algorithm for optimal resource management.

Findings

Significant performance improvements over baseline algorithms.

Effective handling of congested network scenarios.

Convergence of the distributed algorithm in polynomial time.

Abstract

Collaborative edge computing (CEC) is an emerging paradigm where heterogeneous edge devices (stakeholders) 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 a partial-offloading and multi-hop routing model for arbitrarily divisible tasks. Each node individually decides the computation of the received data and the forwarding of data/result traffic. In contrast to most existing works, our model applies to tasks with non-negligible result size, and enables separable data sources and result destinations. We propose a network-wide cost minimization problem with congestion-aware cost to jointly optimize routing and computation offloading. This problem covers various performance metrics and constraints, such as average queueing delay with limited processor capacity. Although the problem is non-convex, we provide non-trivial necessary 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 network topology or task pattern. Numerical evaluation shows that our proposed method significantly outperforms other baseline algorithms in multiple network instances, especially in congested scenarios.