Satellite Communication (SatCom) offers internet connectivity where traditional infrastructures are too expensive to deploy. When using satellites in a geostationary orbit, the distance from Earth forces a round-trip time of at least 550 ms. Coupled with the constrained capacity of the physical link, this challenges the traditional internet access quality we are used to. In this paper, we present a complete passive characterization of the traffic carried by an operational SatCom provider. With this unique vantage point, we observe the performance of the SatCom technology, as well as the usage habits of subscribers in different countries in Europe and Africa. We highlight the implications of such technology on Internet usage and functioning, and we pinpoint technical challenges due to the CDN and DNS resolution issues, while discussing possible optimizations that the ISP could implement to improve the service offered to SatCom subscribers. We complete the characterization of the adoption and performance of newer protocols with a focus on IPv6 and QUIC.

Due to the flourishing development of networks, and abetted by the Covid-19 pandemic, we have witnessed an exponential surge in the global proliferation of Real-Time Communications (RTC) applications in recent years. In light of this, the necessity for robust, scalable, and intelligent network infrastructures and technologies has become increasingly apparent. Among the principal challenges encountered in RTC lies the issue of packet loss. Indeed, the occurrence of losses leads to communication degradation and reallocation that adversely affect the Quality of Experience (QoE). In this paper, we investigate the feasibility of predicting packet loss phenomena through the utilization of machine learning techniques, solely based on statistics derived directly from packets. We provide different definitions of packet loss, subsequently focusing on the most critical scenario, which is defined as the first loss of a series. By delineating the concept of loss, we propose different problem formulations to determine whether there exists a mathematically advantageous scenario over others. To substantiate our analysis, we demonstrate that these phenomena can be correctly identified with a recall up to 66%, leveraging three ample datasets of RTC traffic, which were collected under distinct conditions at different times, further solidifying the validity of our findings.

Satellite Communication (SatCom) is the only choice to access the Internet in remote regions and is characterized by extreme latency and constrained capacity. For SatCom operators, it is thus fundamental to monitor the Quality of Experience (QoE) of subscribers, to measure their satisfaction, spot anomalies and optimize the peculiar network setup. The Web has become the primary source of Internet content, and Web browsing is the main activity of internauts. This paper addresses the challenge of monitoring Web QoE in SatCom environments, proposing a tailored system that employs a supervised approach to predict Web QoE using passive measurements. The system collects training data through Test Agents that mimic real subscribers' traffic patterns and uses them to build Machine Learning (ML) models that predict performance metrics. The findings demonstrate the feasibility of monitoring Web QoE in SatCom environments, with limitations on website applicability and temporal stability. The need for periodic data generation and the development of a general machine learning model for unseen websites remain open challenges. This research contributes to enhancing web browsing experiences in SatCom and expanding understanding of Web QoE monitoring in diverse network settings.

In recent years, an exponential upsurge in the global proliferation of Real-Time Communications (RTC) applications has been witnessed, due to the prosperous development of networks and further fueled by the ramifications of the COVID-19 pandemic. Consequently, the imperative for development of intelligent, resilient, and scalable network infrastructures and technologies has grown significantly. Real-time bitrate prediction could play a crucial role, offering network observability and bolstering proactive system management. By accurately forecasting bitrate, it becomes possible to implement improvements at either application level or network level, such as swift and appropriate bandwidth adaptation. In this paper, we propose a novel Transformer-based deep learning framework called BitFormer designed to predict the short-term bitrate. Our work is based on extensive traffic data collected under various conditions using two prevalent RTC applications, and our model relies solely on packet-level information, which contains the fundamental traffic characteristics and facilitates effortless feature extraction. Through comprehensive evaluations and comparisons, we achieve a superior accuracy of 74% in identifying peak bitrates, while simultaneously ensuring commendable overall performance.

Real-time communication (RTC) platforms have undergone a consistent increase in popularity in recent years, and nowadays, they are fundamental for both work and leisure purposes. To ensure adequate Quality of Experience (QoE) for users of RTC services, we need proper traffic management policies, that, when critical network conditions are detected, react by operating either at the network configuration level or on the application to improve QoE. However, predicting critical network conditions, especially packet losses that are particularly harmful to QoE, is a very challenging task. In this paper, we propose a system for predicting packet losses that might occur in the near future (i.e., in a second) for RTP streaming traffic. We analyze several ML algorithms, from standard techniques to deep neural networks and anomaly detection algorithms, and we apply them to more than 66 hours of data from two popular RTC applications. The selection of the algorithm and its tuning turn out to be fundamental to achieving good performance. In one of the best settings, which are based on a Balanced Random Forest classifier, we obtain a recall of 0.82.

Satellite Communication (SatCom) offers internet connectivity where traditional infrastructures are too expensive to deploy. When using satellites in a geostationary orbit, the distance from Earth forces a round trip time higher than 550 ms. Coupled with the limited and shared capacity of the physical link, this poses a challenge to the traditional internet access quality we are used to.

Real-time communication (RTC) applications have become largely popular in the last decade with the spread of broadband and mobile Internet access. Nowadays, these platforms are a fundamental means for connecting people and supporting businesses that increasingly rely on forms of remote work. In this context, it is of paramount importance to operate at the network level to ensure adequate Quality of Experience (QoE) for users, and appropriate traffic management policies are essential to prioritize RTC traffic. This in turn requires the network to be able to identify RTC streams and the type of content they carry. In this paper, we propose a machine learning-based application to classify media streams generated by RTC applications encapsulated in Secure Real-Time Protocol (SRTP) flows in real-time. Using carefully tuned features extracted from packet characteristics, we train models to classify streams into a variety of classes, including media type (audio/video), video quality, and redundant streams. We validate our approach using traffic from over 62 hours of multi-party meetings conducted using two popular RTC applications, namely Cisco Webex Teams and Jitsi Meet. We achieve an overall accuracy of 96% for Webex and 95% for Jitsi, using a lightweight decision tree model that makes decisions based solely on 1 second of real-time traffic. Our results show that models trained for a particular meeting software have difficulty when used with another one, although domain adaptation techniques facilitate the transfer of pre-trained models.

Real-time communication (RTC) platforms have become increasingly popular in the last decade, together with the spread of broadband Internet access. They are nowadays a fundamental means for connecting people and supporting the economy, which relies more and more on forms of remote working. In this context, it is particularly important to act at the network level to ensure adequate Quality of Experience (QoE) to users, where proper traffic management policies are essential to prioritize RTC traffic. This, in turn, requires in-network devices to identify RTC streams and the type of content they carry. In this paper, we propose a machine learning-based application to classify, in real-time, the media streams generated by RTC applications encapsulated in Secure Real Time Protocol (SRTP) flows. Using carefully tuned features extracted from packet characteristics, we train a model to classify streams into an ample set of classes, including media type (audio/video), video quality and redundant streams. To validate our approach, we use traffic from more than 88 hours of multi-party meeting calls made using the Cisco Webex Teams application. We reach an overall accuracy of 97% with a light-weight decision tree model, which makes decisions using only 1 second of traffic.

The increasing usage of Real-Time Communication (RTC) applications for leisure and remote working calls for realistic and reproducible techniques to test them. They are used under very different network conditions: from high-speed broadband networks, to noisy wireless links. As such, it is of paramount importance to assess the impact of the network on users' Quality of Experience (QoE), especially when it comes to the application's mechanisms such as video quality adjustment or transmission of redundant data. In this work, we pose the basis for a system in which a target RTC application is tested in an emulated mobile environment. To this end, we leverage ERRANT, a data-driven emulator which includes 32 distinct profiles modeling mobile network performance in different conditions. As a use case, we opt for Cisco Webex, a popular RTC application. We show how variable network conditions impact the packet loss, and, in turn, trigger video quality adjustments, impairing the users' QoE.