Convolutional Neural Network Based QoS Prediction with Dimensional Correlation

In recent years, massive services that provide similar functions continue to emerge. Since services sensitive to latency and throughput are often expected to have high Quality of Service (QoS), how to accurately predict QoS has become a challenging issue. Some current deep learning (DL) based approaches usually simply concatenate the embedding vectors, without considering the correlation between embedding dimensions. Besides, the high-order feature interactions are not sufficiently learned. To this end, this paper proposes a Convolutional Neural Network based QoS prediction model with Dimensional Correlation, named QPCN. First, the two dimensional interaction features is explicitly obtained by modeling the embedding vectors. Then, the convolutional neural network is utilized to perform feature extraction and complete QoS prediction. Compared with the fully connected network, QPCN can build a deeper model and learn high-order features. In addition, the parameters of QPCN are significantly reduced, which will reduce the time and energy consumption of inference. The effectiveness of QPCN is validated by experiments on a real-world dataset.