Subgraph Sampling for Inductive Sparse Cloud Services QoS Prediction

Jianlong Xu, Zhiyu Xia, Yuhui Li, Yuxiang Zeng, Zhidan Liu

Abstract: Quality-of-Service (QoS) based collaborative prediction models are emerging to select appropriate edge cloud services for users. Nevertheless, there are still challenges in the realworld QoS prediction task. First, existing QoS prediction models are mostly transductive, failing to generalize to unseen users and services. Secondly, an accurate prediction model remains unexplored under the extreme sparse data scenario, where only a few interactions are available for collaborative filtering. To address these problems, we propose -Inductive -Subgraph -Pattern -Aware Graph Neural Network (ISPA-GNN), which leverages a novel graph-based collaborative filtering method with a subgraph sampling strategy. We further optimize the embeddings components, replacing the user/service embeddings with compositional context information to enable better generalization to unseen nodes while reducing memory usage. Extensive experiments on a large-scale real-world service QoS dataset demonstrate some decent properties of our model, including high prediction accuracy, memory efficiency, and slight performance degradation even if 25% of users/services are never seen.