HSA-Net: Hidden-State-Aware Networks for High-Precision QoS Prediction

Abstract

The high-precision QoS (quality of service) prediction is based on the comprehensive perception of state information of users and services. However, the current QoS prediction approaches have limited accuracy, for most state information of users and services (i.e., network speed, latency, network type, and more) are hidden due to privacy protection. Therefore, this article proposes a hidden-state-aware network (HSA-Net) that includes three steps called hidden state initialization, hidden state perception, and QoS prediction. A hidden state initialization approach is developed first based on the latent dirichlet allocation (LDA). After that, a hidden-state perception approach is proposed to abstract the initialized hidden state by fusing the known information (e.g., service ID and user location). The perception approach consists of four hidden-state perception (HSP) modes (i.e., known mode, object mode, hybrid mode and overall mode) implemented to generate explainable and fused features through four adaptive convolutional kernels. Finally, the relationship between the fused features and the QoS is discovered through a fully connected network to complete the high-precision QoS prediction process. The proposed HSA-Net is evaluated on two real-world datasets. According to the results, the HSA-Net's mean absolute error (MAE) index reduced by 3.67% and 28.84%, whereas the root mean squared error (RMSE) index decreased by 3.07% and 7.14% compared with ten baselines on average in the two datasets.