ChainNet: A Customized Graph Neural Network Model for Loss-Aware Edge AI Service Deployment

Edge AI seeks for the deployment of deep neural network (DNN) based services across distributed edge devices, embedding intelligence close to data sources. Due to capacity constraints at the edge, a difficult challenge lies in planning a dependable deployment that minimizes the data loss rate so as to meet application Quality-of-Service (QoS) goals. In this paper, we present ChainNet, a customized graph neural network (GNN) model serving as a surrogate to assess the reliability of alternative deployments and guide the loss-aware search for an optimal edge AI deployment plan. Extensive results show that ChainNet delivers a substantial improvement in loss prediction accuracy by over 50% compared to established GNN models, such as graph attention networks (GATs). Moreover, we show that ChainNet provides significantly more dependable deployment decisions under a fixed time budget compared to simulation-based search across a spectrum of systems from small to large-scale.