A Two-Stream Light Graph Convolution Network-based Latent Factor Model for Accurate Cloud Service QoS Estimation

Abstract

Historical Quality-of-Service (QoS) data regarding past user-service invocations are vital to understand the user behaviors and cloud service conditions. A Matrix Factorization (MF)-based Collaborative Filtering (CF) model has proven to be highly effective in performing representation learning to such QoS data. However, its performance is hindered by its linear interaction and implicit encoding of collaborative QoS signal. To address this critical issue, this paper presents a Two-stream Light Graph Convolution Network-based latent factor (TLGCN) model with the three-fold ideas: 1) constructing a multilayered and fully-connected network to represent services’ nonlinear latent features; 2) integrating the user-service interactions, i.e., the bipartite graph structure into the representation learning process with a light graph convolution network for illustrating the high-order connectivity information in QoS data; and 3) incorporating the data density-oriented modeling mechanism into the input and output of TLGCN for high computational efficiency. Experimental results on two real QoS datasets demonstrate that the proposed TLGCN model significantly outperforms its state-of-the-art peers in both estimation accuracy for missing QoS data and computational efficiency.