VCCAnalyzer: Identifying Congestion Control Algorithms Used By Video Streaming Platforms

Congestion control algorithms (CCAs) manage data flow in networks to optimize throughput and minimize delay. They are essential for network performance, particularly in applications such as video streaming. Identifying the CCA used by an Internet service provides useful insights into how the interactions between adaptive bitrate streaming algorithms (ABR) and congestion control impact video streaming quality. Prior work, such as CCAnalyzer, classifies a sender’s CCA by passively monitoring network traffic between a third-party service and a controlled receiver.

However, video streaming applications pose additional challenges for these method ologies due to ABR, which introduces off-periods and irregular traffic patterns by limiting the amount of network activity when the video buffer at the client is suffi ciently full. In this thesis, we present VCCAnalyzer, which addresses these challenges by carefully selecting appropriate link rates to minimize ABR interference and apply ing interpolation and smoothing techniques to eliminate drain events, creating clean traces. We explore two classification techniques: (1) Dynamic Time Warping (DTW) as a metric for a 1-nearest neighbor approach, achieving approximately 93% accu racy, and (2) a shapelet-based approach to identify distinctive sub-patterns within traces that serve as discriminative features for CCAs. To demonstrate the efficacy of VCCAnalyzer, we conduct a measurement study examining the CCA deployment across major streaming platforms, including Disney+, Hulu, Twitch, and other com monly visited websites. Our findings reveal some variations in congestion control strategies among these services. Our results demonstrate that VCCAnalyzer can effectively classify CCAs in video environments where traditional methods struggle.