Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/INFOCOM42981.2021.9488851 | IEEE INFOCOM 2021 - IEEE Conference on Computer Communications |
Keywords | DocType | ISSN |
TCP,congestion control,reinforcement learning | Conference | 0743-166X |
ISBN | Citations | PageRank |
978-1-6654-3131-6 | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessio Sacco | 1 | 1 | 2.38 |
| Matteo Flocco | 2 | 4 | 2.08 |
| Flavio Esposito | 3 | 170 | 37.09 |
| Guido Marchetto | 4 | 86 | 20.64 |