ACCT: An Intelligent Congestion Control Mechanism for Future Internet

We propose ACCT, an end-to-end learning-based congestion control mechanism for Internet flows that aims to achieve an optimal sending rate through collaboration between applications and the network elements along the network path. Specifically, an ACCT sender can exploit explicit feedback from the network about its ability to satisfy the application's desired sending rate. Unlike existing schemes, ACCT does not blindly adjust its sending rate to either of these signals; instead, it considers them advisory because the bottleneck may be at routers that do not support ACCT signaling. To detect such bottlenecks and strictly control the end-to-end delay for latency-sensitive applications, ACCT actively measures the end-to-end queuing delay and regulates its sending rate to keep queuing delay within an acceptable margin. Finally, ACCT heavily relies on a Reinforcement Learning (RL) agent to adjust the aggressiveness of its sending rate. We propose a novel approach to formulate the RL agent, exploiting multiple reward functions simultaneously to train the agent. We implemented the ACCT congestion control module in NS-3, which communicates with the RL agent via a technology-agnostic protocol. We evaluated performance across various network scenarios in both wired and wireless networks. Across all experiments, ACCT significantly outperforms commonly used TCP variants such as CUBIC; it provides network fairness when competing with other ACCT flows, and finally, ACCT flows coexist well when they carry heterogeneous traffic.