Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks

ABSTRACT Computational offloading has become an enabling component for edge intelligence in mobile and smart devices. To fill this gap, we propose a low-overhead, congestion-aware distributed task Figure 1: Challenges in distributed multi-hop offloading: (a) probing: offloading scheme by augmenting a distributed greedy framework nodes 1 and 2 query the communication and computing bandwidth with graph-based machine learning. For offloading in wireless multi-hop networks [17-22], a centralized scheduler with global knowledge of 1. INTRODUCTION However, centralized multihop The proliferation of mobile and smart devices enables the collection offloading has the drawbacks of single-point-of-failure and poor of rich sensory data from both physical and cyber spaces, leading to scalability, due to the high communication overhead of collecting the many exciting applications, such as connected vehicles, drone/robot full network state to a dedicated scheduler. Distributed multi-hop offloading swarms, software-defined networks (SDN), and Internet-of-Things based on pricing [18,21] and learning [22] only focus on the (IoT) [1-4]. To support these applications, wireless multi-hop networks, capacity of servers, while ignoring the potential network congestion which have been traditionally used for military communications, caused by offloading [19], as illustrated by the motivating example disaster relief, and sensor networks, are now envisioned in Figure 1.