Space-Time-Aware Proactive QoS Monitoring for Mobile Edge Computing

This paper presents a novel probabilistic Quality of Service (QoS) monitoring method named DLSTM-BRPM (Double Long Short Term Memory (DouLSTM-Den) based Bayesian Runtime Proactive Monitoring) to accurately and efficiently monitor QoS in a mobile edge environment. This method consists of a DouLSTM-Den model and a Gaussian Hidden Bayesian classifier. The DouLSTM-Den model aims to predict a user's future movement trajectory in real time and proactively monitor the spatio-temporal QoS performance of services based on the predicted trajectory. The Gaussian Hidden Bayesian classifier is employed to accurately monitor QoS by constructing parent attributes to reduce the interdependence between QoS attributes. Our experiments based on public synthetic datasets demonstrate the effectiveness of the proposed method over state-of-the-art solutions. We also conducted experiments in a real-world edge environment to validate the feasibility of the proposed method.