QoS Prediction via Multi-scale Feature Fusion Based on Convolutional Neural Network

Quality of Service (QoS) prediction is a crucial aspect in service management. However, the existing QoS prediction methods face several limitations, such as loss of information during encoding, incomplete feature extraction and neglect of the interaction between features. To this end, this paper proposes a new QoS PRediction method based on aMulti-Scale convolutional neural Network, i.e., QPRMSN. For each service invocation, we build a feature matrix that encodes invocation context and QoS characteristics by using status codes with degrees of membership. Then, a multi-scale convolutional neural network is employed to extract features that keep detailed information during deep global features mining. Moreover, we introduce attention mechanism to learn the intrinsic relationships between features to strengthen key features. Finally, QPRMSN completes the QoS prediction based on a multi-level feature matrix. Extensive experiments are conducted on a real-world dataset to evaluate the performance of QPRMSN. The experimental results demonstrate that QPRMSN outperforms the state-of-the-art QoS prediction models and is better at QoS context encoding.