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A Deep Reinforcement Learning Perspective on Internet Congestion Control
Nathan Jay · Noga H. Rotman · Brighten Godfrey · Michael Schapira · Aviv Tamar

Wed Jun 12 02:35 PM -- 02:40 PM (PDT) @ Hall B

We present and investigate a novel and timely application domain for deep reinforcement learn-ing (RL): Internet congestion control. Congestion control is the core networking task of modulating traffic sources’ data-transmission rates so as to efficiently utilize network capacity. Congestion control is fundamental to computer networking research and practice, and has recently been the subject of extensive attention in light of the advent of Internet services such as live video, augmented and virtual reality, Internet-of-Things, and more. We show that casting congestion control as an RL task enables the training of deep network policies that capture intricate patterns in data traffic and network conditions, and leveraging this to outperform state-of-the-art congestion control schemes.Alongside these promising positive results, we also highlight significant challenges facing real-world adoption of RL-based congestion control solutions, such as fairness, safety, and generalization, which are not trivial to address within conventional RL formalism. To facilitate further research of these challenges and reproducibility of our results, we present a test suite for RL-guided congestion control based on the OpenAI Gym interface.

Author Information

Nathan Jay (University of Illinois Urbana-Champaign)
Noga H. Rotman (Hebrew University of Jerusalem)
Brighten Godfrey (University of Illinois Urbana-Champaign)
Michael Schapira (Hebrew University of Jerusalem)
Aviv Tamar (Technion)

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