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.File | Dimensione | Formato | |
---|---|---|---|
INFOCOM 2021-camera ready.pdf
accesso aperto
Descrizione: Articolo principale
Tipologia:
2. Post-print / Author's Accepted Manuscript
Licenza:
PUBBLICO - Tutti i diritti riservati
Dimensione
479.94 kB
Formato
Adobe PDF
|
479.94 kB | Adobe PDF | Visualizza/Apri |
Owl_Congestion_Control_with_Partially_Invisible_Networks_via_Reinforcement_Learning.pdf
non disponibili
Descrizione: Articolo principale
Tipologia:
2a Post-print versione editoriale / Version of Record
Licenza:
Non Pubblico - Accesso privato/ristretto
Dimensione
2.09 MB
Formato
Adobe PDF
|
2.09 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2862067