QM-RGNN: An Efficient Online QoS Measurement Framework with Sparse Matrix Imputation for Distributed Edge Clouds

Measurements for the quality of end-to-end network services (QoS) are crucial to ensure stability, reliability, and user experience for distributed edge clouds. Measuring all QoS data brings significant costs. Existing QoS measurement methods attempt to use sparse measured QoS data to estimate unmeasured QoS data. But they suffer from limited estimation accuracy when facing QoS data with high sparsity or significant volatility. Moreover, they also consume high sampling and training costs during continuously online measurements. Our preliminary analysis reveals that end-to-end QoS is strongly temporal-spatial related. It inspires us to leverage partially measured QoS data to impute temporal-spatial-related unmeasured QoS data for reducing measurement costs. To predict unmeasured QoS data precisely with low computational costs, we propose a novel QoS Measurement framework based on Residual Graph Neural Network (QM-RGNN), which inputs QoS data as a graph and outputs the prediction of unmeasured QoS data. It consists of three core components: 1) an encoder-decoder model QM-GNN with GCN as the encoder and MLP as the decoder is devised for efficient QoS prediction, and a residual module is introduced in QM-GNN to tackle highly sparse and volatile QoS data; 2) a dynamic adaptive sample ratio is proposed to reduce the sampling costs; 3) an online learning pattern is designed to reduce continuous training costs. Experiments on two real-world industrial edge cloud datasets demonstrate the superiority of QM-RGNN in QoS measurement. It obtains at least a 37.5% reduction of relative RMSE between ground-truth and predicted QoS data with up to 90% training cost reduction and 22.7% sampling cost reduction.