Title: CCAnalyzer: An Efficient and Nearly-Passive Congestion Control Classifier
Authors: Ranysha Ware, Adithya Abraham Philip, Nicholas Hungria,
Yash Kothari, Justine Sherry, Srinivasan Seshan (Carnegie Mellon University)
Scribe: Hongyu Du (Xiamen University)
Background:
The rapid development of the Internet has brought a variety of network applications and data transmission needs. TCP, as the core protocol of the transport layer, its congestion control algorithm directly affects the transmission efficiency and stability of the network. With the continuous evolution of the Internet, new CCAs emerge one after another, which have a profound impact on network performance. Understanding and identifying these CCAs is crucial for designing better network systems and improving data transmission efficiency.
Key idea and contribution:
The emergence of CCAnalyzer technology has provided a new perspective for identifying and analyzing various CCAs, helping network administrators and researchers to better understand and optimize networks. CCAnalyzer creates local bottleneck links to observe and record queue occupancy, using this as the foundational data for classification algorithms. Leveraging the flexibility of the DTW algorithm, CCAnalyzer can process and compare time series data, maintaining classification accuracy even when network conditions change. Through meticulous experimentation and analysis, CCAnalyzer has determined the optimal network configuration parameters, including bandwidth, RTT, and queue size, ensuring efficient and accurate classification in different network environments. Highlights of CCAnalyzer technology include:
- Efficiency: CCAnalyzer analyzes the time series of queue sizes using the advanced DTW algorithm to quickly identify CCAs, significantly increasing the speed of classification.
- Almost Passive: Compared to traditional active probing methods, CCAnalyzer reduces network interference and performs measurements in an almost passive manner, minimizing the impact on the performance of the system under test.
- Openness: CCAnalyzer is capable of not only identifying existing CCAs but also discovering unknown new algorithms, providing strong monitoring and adaptation capabilities for the continuous development of the Internet.
Evaluation:
In extensive experiments, CCAnalyzer has demonstrated outstanding classification accuracy, being able to correctly identify all tested CCAs, including some of the latest deployed algorithms such as BBRv3. Compared to existing classification methods, CCAnalyzer has significantly improved in terms of data transfer volume and time efficiency, reducing the resource consumption during the measurement process.
Q: I would like to know, based on your measurements and research, if there has been a noticeable change in people’s preferences for the use of CCA? What are your insights on these changes?
A: Our measurement study finds that 54% of the 5215 websites that are possible to classify, are classified as using BBRv1 and that only 6.9% are classified as Cubic. Comparing this finding to the results reported from Gordon and IG studies, we see increasing deployment of BBR and decreases in Cubic. These results suggest a change in the most widely-deployed CCA from Cubic to BBR. This would represent a significant shift from an Internet dominated by loss-based congestion control to something else entirely. This shift may, inturn, impact the design of many Internet systems and components.
Personal thoughts:
CCAnalyzer leverages DTW technology to break through the limitations of traditional congestion control algorithm identification methods based on CWND estimation. This innovation not only enhances the accuracy of classification but also reduces interference with the network, which is crucial for maintaining network stability and performance. CCAnalyzer’s ability to identify unknown congestion control algorithms is particularly important in today’s rapidly advancing technology landscape. As new algorithms continue to emerge, this feature of CCAnalyzer ensures its adaptability to the changes in future network environments.