When machine learning meets congestion control: A survey and comparison
Abstract Machine learning has seen a significant surge and uptake across many diverse
applications. The high flexibility, adaptability, and computing capabilities it provides extend …
applications. The high flexibility, adaptability, and computing capabilities it provides extend …
Eagle: Refining congestion control by learning from the experts
Traditional congestion control algorithms were designed with a hardwired heuristic mapping
between packet-level events and predefined control actions in response to these events …
between packet-level events and predefined control actions in response to these events …
Pareto: Fair congestion control with online reinforcement learning
Modern-day computer networks are highly diverse and dynamic, calling for fair and adaptive
network congestion control algorithms with the objective of achieving the best possible …
network congestion control algorithms with the objective of achieving the best possible …
Congestion control: A renaissance with machine learning
W Wei, H Gu, B Li - IEEE network, 2021 - ieeexplore.ieee.org
In the past several decades, it has been well known that the Transmission Control Protocol
(TCP), even with its modern variants such as CUBIC, may not perform optimally when …
(TCP), even with its modern variants such as CUBIC, may not perform optimally when …
{AUTO}: Adaptive congestion control based on {Multi-Objective} reinforcement learning for the {Satellite-Ground} integrated network
The satellite-ground integrated network is highly heterogeneous with diversified
applications. It requires congestion control (CC) to achieve consistent high performances in …
applications. It requires congestion control (CC) to achieve consistent high performances in …
Towards AI-enabled traffic management in multipath TCP: A survey
Numerous applications on the web use transmission control protocol (TCP) as a transport
protocol to ensure efficient and fair sharing of network resources among users. With the …
protocol to ensure efficient and fair sharing of network resources among users. With the …
Reinforcement learning for bandwidth estimation and congestion control in real-time communications
Bandwidth estimation and congestion control for real-time communications (ie, audio and
video conferencing) remains a difficult problem, despite many years of research. Achieving …
video conferencing) remains a difficult problem, despite many years of research. Achieving …
Tcp-neuroc: Neural adaptive tcp congestion control with online changepoint detection
W Li, S Gao, X Li, Y Xu, S Lu - IEEE Journal on Selected Areas …, 2021 - ieeexplore.ieee.org
Congestion control is a fundamental mechanism for TCP protocol, which has been
extensively studied in the past three decades. However, our experimental evaluations show …
extensively studied in the past three decades. However, our experimental evaluations show …
RLQ: Workload allocation with reinforcement learning in distributed queues
Distributed workload queues are nowadays widely used due to their significant advantages
in terms of decoupling, resilience, and scaling. Task allocation to worker nodes in distributed …
in terms of decoupling, resilience, and scaling. Task allocation to worker nodes in distributed …
Partially oblivious congestion control for the internet via reinforcement learning
A Sacco, M Flocco, F Esposito… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …
end congestion control has not been solved. This debate is due to the variance of conditions …