Dynamic TCP Initial Windows and Congestion Control Schemes Through Reinforcement Learning

Nie, Xin; Zhao, Youjian; Li, Zhihan; Chen, Guo; Sui, Kaixin; Zhang, Jiyang; Ye, Zijie; Pei, Dan

doi:10.1109/jsac.2019.2904350

Cited by 93 publications

(23 citation statements)

References 29 publications

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“…For a long flow service, the conventional manually- and statically-configured congestion control is not able to fulfill the latest network performance requirements. To address the above two challenges, Nie et al [ 23 ] proposed a TCP reinforcement learning (RL) technique, called TCP-RL. The TCP-RL dynamically adjusts the IW and congestion control to improve TCP flow transmission efficiency.…”

Section: Tcp and Mptcpmentioning

confidence: 99%

A Brief Review of Multipath TCP for Vehicular Networks

Chao

Yoshinaga

et al. 2021

Sensors

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Multipath TCP (MPTCP) is one of the most important extensions to TCP that enables the use of multiple paths in data transmissions for a TCP connection. In MPTCP, the end hosts transmit data across a number of TCP subflows simultaneously on one connection. MPTCP can sufficiently utilize the bandwidth resources to improve the transmission efficiency while providing TCP fairness to other TCP connections. Meanwhile, it also offers resilience due to multipath data transfers. MPTCP attracts tremendous attention from the academic and industry field due to the explosive data growth in recent times and limited network bandwidth for each single available communication interface. The vehicular Internet-of-Things systems, such as cooperative autonomous driving, require reliable high speed data transmission and robustness. MPTCP could be a promising approach to solve these challenges. In this paper, we first conduct a brief survey of existing MPTCP studies and give a brief overview to multipath routing. Then we discuss the significance technical challenges in applying MPTCP for vehicular networks and point out future research directions.

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Section: Tcp and Mptcpmentioning

confidence: 99%

A Brief Review of Multipath TCP for Vehicular Networks

Chao

Yoshinaga

et al. 2021

Sensors

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“…However, all these algorithms are optimized for very simple network topologies and situations and strongly depend on the considered scenario, so to the best of our knowledge there is no fully implemented and well-tested congestion control mechanism using these techniques. Along this line, the authors of [105] investigate a reinforcement learning approach to improve the performance of both short and long TCP flows. Figure 11: Overview of the operating points in the states of the BBR algorithm…”

Section: E Machine Learning Approachesmentioning

confidence: 99%

A Survey on Recent Advances in Transport Layer Protocols

Polese

Chiariotti

Bonetto

et al. 2019

IEEE Commun. Surv. Tutorials

116

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Over the years, the Internet has been enriched with new available communication technologies, for both fixed and mobile networks and devices, exhibiting an impressive growth in terms of performance, with steadily increasing available data rates. The Internet research community has kept trying to evolve the transport layer protocols to match the capabilities of modern networks, in order to fully reap the benefits of the new communication technologies. This paper surveys the main novelties related to transport protocols that have been recently proposed, identifying three main research trends: (i) the evolution of congestion control algorithms, to target optimal performance in challenging scenarios, possibly with the application of machine learning techniques; (ii) the proposal of brand new transport protocols, alternative to the Transmission Control Protocol (TCP) and implemented in the user-space; and (iii) the introduction of multipath capabilities at the transport layer.

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“…The Learning-Based Protocols. TCP Remy [16], PCC [8], Vivace [4], Copa [5], TCP-RL [17], and QTCP [18] are learning-based protocols. Machine-learning methods will be more and more important for the TCP protocol designers.…”

Section: The Hybrid Protocolsmentioning

confidence: 99%

Learn-ing-Based On-AP TCP Performance Enhancement

Lin

Jiang

2020

Wireless Communications and Mobile Computing

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Data transmissions suffer from TCP’s poor performance since the introduction of the first commercial wireless services in the 1990s. Recent years have witnessed a surge of academia and industry activities in the field of TCP performance optimization. For a TCP flow whose last hop is a wireless link, congestions in the last hop dominate its performance. We implement an integral data sampling, network monitoring, and rate control software-defined wireless networking (SDWN) system. By analysing our sampled data, we find that there exist strong relationships between congestion packet loss behaviors and the instant cross-layer network metric measurements (states). We utilize these qualitative relationships to predict future congestions in wireless links and enhance TCP performance by launch necessary rate control locally on the access points (AP) before the congestions. We also implement modeling and rate control modules on this platform. Our platform senses the instant wireless dynamic and takes actions promptly to avoid future congestions. We conduct real-world experiments to evaluate its performance. The experiment results show that our methods outperform the bottleneck bandwidth and RTT (BBR) protocol and a recently proposed protocol Vivace on throughput, delay, and jitter performance at least 16.5%, 25%, and 12.6%, respectively.

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Dynamic TCP Initial Windows and Congestion Control Schemes Through Reinforcement Learning

Cited by 93 publications

References 29 publications

A Brief Review of Multipath TCP for Vehicular Networks

A Brief Review of Multipath TCP for Vehicular Networks

A Survey on Recent Advances in Transport Layer Protocols

Learn-ing-Based On-AP TCP Performance Enhancement

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