Performance comparison of congestion control strategies for multi-path TCP in the NORNET testbed

Fa, Fu; Zhou, Xing; Dreibholz, Thomas; Wang, Keying; Zhou, Feng; Gan, Quan

doi:10.1109/iccchina.2015.7448667

Cited by 9 publications

(7 citation statements)

References 16 publications

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“…However, these algorithms are based on the legacy TCP Reno CC algorithm and follow the AIMD scheme, and as such, they cannot satisfy previously presented three design goals for operation in a 5G mmWave environment [ 66 ]. Since the aforementioned coupled CC algorithms are primarily focused on the increase of the cwnd, while neglecting the cwnd decreasing mechanism [ 67 ] ( Figure 4 ), authors in Ref. [ 68 ] proposed a loss-based MP-TCP CC algorithm called Dynamic-LIA (D-LIA).…”

Section: Tcp Congestion Controlmentioning

confidence: 99%

A Comprehensive Overview of TCP Congestion Control in 5G Networks: Research Challenges and Future Perspectives

Lörincz

Klarin

Ožegović

2021

Sensors

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In today’s data networks, the main protocol used to ensure reliable communications is the transmission control protocol (TCP). The TCP performance is largely determined by the used congestion control (CC) algorithm. TCP CC algorithms have evolved over the past three decades and a large number of CC algorithm variations have been developed to accommodate various network environments. The fifth-generation (5G) mobile network presents a new challenge for the implementation of the TCP CC mechanism, since networks will operate in environments with huge user device density and vast traffic flows. In contrast to the pre-5G networks that operate in the sub-6 GHz bands, the implementation of TCP CC algorithms in 5G mmWave communications will be further compromised with high variations in channel quality and susceptibility to blockages due to high penetration losses and atmospheric absorptions. These challenges will be particularly present in environments such as sensor networks and Internet of Things (IoT) applications. To alleviate these challenges, this paper provides an overview of the most popular single-flow and multy-flow TCP CC algorithms used in pre-5G networks. The related work on the previous examinations of TCP CC algorithm performance in 5G networks is further presented. A possible implementation of TCP CC algorithms is thoroughly analysed with respect to the specificities of 5G networks, such as the usage of high frequencies in the mmWave spectrum, the frequent horizontal and vertical handovers, the implementation of the 5G core network, the usage of beamforming and data buffering, the exploitation of edge computing, and the constantly transmitted always-on signals. Moreover, the capabilities of machine learning technique implementations for the improvement of TCPs CC performance have been presented last, with a discussion on future research opportunities that can contribute to the improvement of TCP CC implementation in 5G networks. This survey paper can serve as the basis for the development of novel solutions that will ensure the reliable implementation of TCP CC in different usage scenarios of 5G networks.

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Section: Tcp Congestion Controlmentioning

confidence: 99%

A Comprehensive Overview of TCP Congestion Control in 5G Networks: Research Challenges and Future Perspectives

Lörincz

Klarin

Ožegović

2021

Sensors

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“…It measures the application payload throughput performance of different transport connections and protocols. In particular, MPTCP was supported by NetPerfMeter as well [33,34]. Furthermore, it allowed us to configure the send and receive buffer sizes for a connection by using the SO_SNDBUF and SO_RCVBUF socket options.…”

Section: Measurement Scenariomentioning

confidence: 99%

A Multi-Parameter Comprehensive Optimized Algorithm for MPTCP Networks

et al. 2021

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The transmission performance of a multi-path transmission control protocol (MPTCP) is affected by many parameters, such as path management, congestion control, buffer size, and subflow bandwidth. Most of the previous studies have focused almost exclusively on the improvement of a single parameter, without a holistic view. In this paper, a multi-parameter comprehensive optimized algorithm (MPCOA) is proposed to comprehensively optimize the above parameters. The MPCOA algorithm can find a smaller buffer size and select an appropriate congestion control and path management algorithm on the premise of ensuring better throughput. Experiments in three scenarios show that MPCOA can save buffer space and subflow resources, and achieve a high throughput. Meanwhile, a set of quantitative improvement results given by MPCOA is convenient for us to evaluate the quality of the MPTCP network and provide reference for our ongoing future work.

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“…The key difference is that instead of growing at a rate of one packet (MSS) per RTT, the pace at which each subflow's congestion window grows depends on the sum of all subflows' congestion windows [58]. This "coupling" between subflows ensures that MPTCP achieves its goals (see above) [22].…”

Section: Background and Goalsmentioning

confidence: 99%

MPCC

Gilad

Rozen-Schiff

Godfrey

et al. 2020

Proceedings of the 16th International Conference on Emerging Networking EXperiments and Technologies

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Multipath transport, as embodied in MPTCP, is deployed to improve throughput and reliability in mobile and residential access networks, with additional use-cases including spreading load in data centers and WANs. However, MPTCP is fundamentally tied to TCP Reno's legacy AIMD algorithm, and significantly lags behind the performance of modern single-path designs. Consequently, MPTCP fails to achieve high performance in many real-world environments. We present MPCC, a high-performance multipath congestion control architecture. To achieve our combined goals of fairness and high performance in challenging environments, MPCC employs online convex optimization (a.k.a. online learning). In experiments with a kernel implementation on emulated and live networks, MPCC significantly outperforms MPTCP.

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Performance comparison of congestion control strategies for multi-path TCP in the NORNET testbed

Cited by 9 publications

References 16 publications

A Comprehensive Overview of TCP Congestion Control in 5G Networks: Research Challenges and Future Perspectives

A Comprehensive Overview of TCP Congestion Control in 5G Networks: Research Challenges and Future Perspectives

A Multi-Parameter Comprehensive Optimized Algorithm for MPTCP Networks

MPCC

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