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.

Where Did My Packet Go? Real-Time Prediction of Losses in Networks / Song, Tailai; Markudova, Dena; Perna, Gianluca; Meo, Michela. - ELETTRONICO. - (2023), pp. 3836-3841. (Intervento presentato al convegno ICC 2023 - IEEE International Conference on Communications tenutosi a Rome, Italy nel 28 May 2023 - 01 June 2023) [10.1109/ICC45041.2023.10278583].

Where Did My Packet Go? Real-Time Prediction of Losses in Networks

Song, Tailai;Markudova, Dena;Perna, Gianluca;Meo, Michela
2023

Abstract

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.
2023
978-1-5386-7462-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2983314