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Improving TCP Congestion Control with Machine Intelligence

In a TCP/IP network, a key to ensure efficient and fair sharing of network resources among its users is the TCP congestion control (CC) scheme. Previously, the design of TCP CC schemes is based on hard-wiring of predefined actions to specific feedback signals from the network. However, as networks become more complex and dynamic, it becomes harder to design the optimal feedback-action mapping. Recently, learning-based TCP CC schemes have attracted much attention due to their strong capabilities to learn the actions from interacting with the network. In this paper, we design two learning-based TCP CC schemes for wired networks with under-buffered bottleneck links, a loss predictor (LP) based TCP CC (LP-TCP), and a reinforcement learning (RL) based TCP CC (RL-TCP). We implement both LP-TCP and RL-TCP in NS2. Compared to the existing NewReno and Q-learning based TCP, LP-TCP and RL-TCP both achieve a better tradeoff between throughput and delay, under various simulated network scenarios.