Partially Oblivious Congestion Control for the Internet via Reinforcement Learning

Sie sind hier: Homepage > Suche

Partially Oblivious Congestion Control for the Internet via Reinforcement Learning

Freier Zugriff

Alessio Sacco / Matteo Flocco / Flavio Esposito / Guido Marchetto

Despite years of research on transport protocols, the tussle between in-network and end-to-end congestion control has not been solved. 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.

Zugriff

Download

Exportieren, teilen und zitieren

Dokumentinformationen

Titel :

Partially Oblivious Congestion Control for the Internet via Reinforcement Learning

Beteiligte:

Alessio Sacco (Autor:in) / Matteo Flocco (Autor:in) / Flavio Esposito (Autor:in) / Guido Marchetto (Autor:in) / Sacco, Alessio / Flocco, Matteo / Esposito, Flavio / Marchetto, Guido

Erscheinungsdatum :

2023-01-01

DOI :

https://doi.org/10.1109/TNSM.2022.3215669

Medientyp :

Aufsatz (Zeitschrift)

Format :

Elektronische Ressource

Sprache :

Englisch

Schlagwörter :

TCP , congestion control , reinforcement learning