DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control

Author:

Pokhrel Shiva Raj1ORCID,Kua Jonathan1ORCID,Satish Deol1,Ozer Sebnem2,Howe Jeff2,Walid Anwar3

Affiliation:

1. IoT Research Lab, Deakin University, Geelong 3220, Australia

2. Comcast Corporation, Philadelphia, PA 19103, USA

3. Amazon Science, New York, NY 10001, USA

Abstract

We introduce a novel multipath data transport approach at the transport layer referred to as ‘Deep Deterministic Policy Gradient for Multipath Performance-oriented Congestion Control’ (DDPG-MPCC), which leverages deep reinforcement learning to enhance congestion management in multipath networks. Our method combines DDPG with online convex optimization to optimize fairness and performance in simultaneously challenging multipath internet congestion control scenarios. Through experiments by developing kernel implementation, we show how DDPG-MPCC performs compared to the state-of-the-art solutions.

Funder

COMCAST Innovation Fund

Publisher

MDPI AG

Subject

Computer Networks and Communications

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3. Dong, M., Meng, T., Zarchy, D., Arslan, E., Gilad, Y., Godfrey, B., and Schapira, M. (2018, January 9–11). PCC Vivace: Online-Learning Congestion Control. Proceedings of the 15th USENIX Symposium on Networked Systems Design and Implementation (NSDI 18), Renton, WA, USA.

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5. Gilad, T., Rozen-Schiff, N., Godfrey, P.B., Raiciu, C., and Schapira, M. (2020, January 1–4). MPCC: Online learning multipath transport. Proceedings of the 16th International Conference on emerging Networking EXperiments and Technologies, Barcelona, Spain.

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