One Is Enough: Efficient Modeling of RTP Traffic for QoS Predictions in Real-Time Communications

In recent years, we have witnessed an unprecedented upsurge in popularity and advancement of Real-time Transport Protocol (RTP)-based real-time communication (RTC) applications. For the sake of their optimizations, Quality of Service (QoS) prediction serves as a viable venue for enhancing network monitoring and enabling preemptive solutions. However, existing methodologies are typically tailored and constrained to individual traffic flows and QoS metrics, lagging in correlation capturing and computational efficiency. In light of this, we argue that "one model is enough" to conquer these challenges, and propose a novel deep learning (DL) framework namely Oh, employing a teacher-student scheme with two training stages. The first (teacher) involves a sophisticated Long Short-Term Memory (LSTM) neural network (NN) empowered by a customized attention structure, and the second (student) comprises simple feedforward NNs to distill knowledge and reduce complexity. Specifically, Oh leverages a multi-task learning paradigm, mapping extracted features to four key QoS indicators. It is capable of simultaneously handling unlimited amount of concurrent RTP flows with packet-level information and performing end-to-end predictions of multiple QoS metrics in one single shot. Our work is based on massive traffic collected during real video-teleconferencing calls using various software, and benchmarked against multiple other machine learning (ML)/DL algorithms. As a result, Oh-teacher yields superior prediction performance, whereas Oh-student achieves distinctly enhanced temporal efficiency with comparable forecasting outcomes.