Hoeffding Regression Trees for Forecasting Quality of Experience in B5G/6G Networks

Online data stream analysis is becoming more and more relevant, as the focus of daily life analyses shifts from offline processing to real-time acquisition and modeling of massive data from remote devices. In this paper, we focus our attention on the domain of telecommunications, in particular the video streaming services for moving devices (e.g., a passenger enjoying a movie during a car trip). Since the streaming service must provide a satisfactory level of quality of experience to the user, it is important to predict incoming problems on video quality. We used the well-known Hoeffding Decision Tree (HDT) for streaming data, tailored to regression problems, and we compared its performance with standard Regression Trees (RTs) to evaluate the potentiality of HDTs to forecast the quality of experience in terms of accuracy, time for learning, and memory used. Results show that, during the online learning process, the standard RT outperforms HDT in terms of accuracy, but is prone to under-performance in terms of timings and memory when applied to potentially massive data streaming scenarios.