P4CCI: P4-based Online TCP Congestion Control Algorithm Identification for Traffic Separation

Congestion Control Algorithms (CCAs) regulate the sending rates of hosts to avoid congestion in the network. Studies have shown that when flows belonging to different CCAs co-exist on the same link, their shares on that link are significantly different. If the CCAs of active flows can be determined on live traffic, then flows belonging to the same CCA can be allocated into a dedicated queue. Unfortunately, identifying the CCA at line rate is not straightforward since the CCA is not advertised in the header fields of a packet. Moreover, with Gigabits per second (Gbps) traffic crossing a network, analyzing each packet to infer the CCA is not possible, especially with general-purpose CPUs. This paper proposes P4CCI, a system that detects the CCA of a flow at line rate by leveraging Programmable Data Planes (PDP). The PDP computes and extracts the flow's bytes-in-flight and sends them to a Deep Learning model for classification. Once classified, the flows are allocated into dedicated queues based on their CCA type. The system was implemented and tested on real hardware that uses Intel's Tofino ASIC. The experiments were executed on traffic provided by CAIDA. Results show that P4CCI can detect the CCAs with high accuracy. Furthermore, the performance of the network is greatly improved when the flows are separated by their CCAs.