Optimal Congestion-aware Routing and Offloading in Collaborative Edge Computing

TL;DR

This paper introduces a congestion-aware routing and offloading strategy for collaborative edge computing that optimizes resource sharing and reduces delays in complex, arbitrary network topologies.

Contribution

It formulates a novel convex cost-based optimization model for partial-offloading and multi-hop routing with non-negligible result sizes, providing a distributed algorithm for optimal solutions.

Findings

Significant performance improvements over baseline algorithms.

Effective in congested network scenarios.

Converges to global optimum in polynomial time.

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, 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.