With the exponential growth of Internet of Things (IoT) devices, edge
computing (EC) is gradually playing an important role in providing
cost-effective services. Cependant, existing approaches struggle to perform well
in graph-structured scenarios where user data is correlated, such as traffic
flow prediction and social relationship recommender systems. In particular,
graph neural network (GNN)-based approaches lead to expensive server
communication cost. To address this problem, we propose GraphEdge, an efficient
GNN-based EC architecture. It considers the EC system of GNN tasks, where there
are associations between users and it needs to take into account the task data
of its neighbors when processing the tasks of a user. Specifically, the
architecture first perceives the user topology and represents their data
associations as a graph layout at each time step. Then the graph layout is
optimized by calling our proposed hierarchical traversal graph cut algorithm
(HiCut), which cuts the graph layout into multiple weakly associated subgraphs
based on the aggregation characteristics of GNN, and the communication cost
between different subgraphs during GNN inference is minimized. Finally, based
on the optimized graph layout, our proposed deep reinforcement learning (DRL)
based graph offloading algorithm (DRLGO) is executed to obtain the optimal
offloading strategy for the tasks of users, the offloading strategy is
subgraph-based, it tries to offload user tasks in a subgraph to the same edge
server as possible while minimizing the task processing time and energy
consumption of the EC system. Experimental results show the good effectiveness
and dynamic adaptation of our proposed architecture and it also performs well
even in dynamic scenarios.

Cet article explore les excursions dans le temps et leurs implications.

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