QoS prediction in intelligent edge computing based on feature learning

With the development of 5G and 6G, more computing and network resources on edge nodes are deployed close to the terminal. Meanwhile, the number of smart devices and intelligent services has grown significantly, which makes it difficult for users to choose a suitable service. The rich contextual information plays an important role in the prediction of service quality. In this paper, we propose a quality of service(QoS) prediction approach based on feature learning, the contextual information represented as the explicit features and underlying relationship hidden in the implicit features are fully considered. Then, the multi-head self-attention mechanism is used in the interacting layer to determine which features should be combined to form meaningful high-order features interaction. We have implemented our proposed approach with experiments based on real-world datasets. Experimental results show that our approach achieved a better performance of service QoS prediction in an intelligent edge computing environment for future communication.