Contains fulltext : 331392.pdf (Publisher’s version ) (Open Access)

Video Conferencing Applications (VCAs) are widely used in education and remote work. Realistic modeling of VCAs, especially proprietary VCAs, enables simulation of VCA behaviors under network conditions that are different from those where the VCAs are deployed. Models of VCAs can be useful for both network operators and VCA developers who can assess how sensitive a VCA is to changes in the network. For instance, if VCA A begins to suffer video quality degradation when end-to-end delay exceeds 100 ms, while VCA B's corresponding threshold is 200 ms, operators can decide to give priority to A's traffic for better overall user experience. Additionally, modeling helps VCA designers select parameter values to navigate inherent performance trade-offs. If a model can quantify, for instance, how increasing a certain threshold to enhance video quality stability may increase frame latency, designers can make informed choices.

Internet service providers often fingerprint the type of application, e.g., video streaming or video conferencing, from a network traffic trace, for security and traffic engineering purposes. Several traffic classification methods have been proposed to apply machine learning techniques to packet timestamp and packet size information in traffic traces to perform such fingerprinting. However, classification accuracy drops when the machine learning methods are trained on traces that do not capture diverse end point and underlying network conditions. In this work, we compare a number of augmentation techniques that attempt to mitigate this problem, and evaluate their effectiveness when we systematically change specific end point and underlying network conditions. We find that the Rosetta traffic classification method that uses packet size sequences benefits much more from current augmentation methods than the mini-FlowPic method that uses both packet timestamps and sizes. We believe that there is a critical need for customized augmentations that enrich traffic traces, while being faithful to behaviors of real users and real protocols.1