Years of research on transport protocols have not solved the tussle between in-network and end-to-end congestion control. This debate is due to the variance of conditions and assumptions in different network scenarios, e.g., cellular versus data center networks. Recently, the community has proposed a few transport protocols driven by machine learning, nonetheless limited to end-to-end approaches. In this paper, we present Owl, a transport protocol based on reinforcement learning, whose goal is to select the proper congestion window learning from end-to-end features and network signals, when available. We show that our solution converges to a fair resource allocation after the learning overhead. Our kernel implementation, deployed over emulated and large scale virtual network testbeds, outperforms all benchmark solutions based on end-to-end or in-network congestion control.

Owl: Congestion Control with Partially Invisible Networks via Reinforcement Learning / Sacco, Alessio; Flocco, Matteo; Esposito, Flavio; Marchetto, Guido. - ELETTRONICO. - (2021). (Intervento presentato al convegno IEEE INFOCOM 2021 - IEEE Conference on Computer Communications tenutosi a Virtual Event nel 10-13 May 2021) [10.1109/INFOCOM42981.2021.9488851].

Owl: Congestion Control with Partially Invisible Networks via Reinforcement Learning

Alessio Sacco;Guido Marchetto
2021

Abstract

Years of research on transport protocols have not solved the tussle between in-network and end-to-end congestion control. This debate is due to the variance of conditions and assumptions in different network scenarios, e.g., cellular versus data center networks. Recently, the community has proposed a few transport protocols driven by machine learning, nonetheless limited to end-to-end approaches. In this paper, we present Owl, a transport protocol based on reinforcement learning, whose goal is to select the proper congestion window learning from end-to-end features and network signals, when available. We show that our solution converges to a fair resource allocation after the learning overhead. Our kernel implementation, deployed over emulated and large scale virtual network testbeds, outperforms all benchmark solutions based on end-to-end or in-network congestion control.
2021
978-1-6654-3131-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2862067