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Improving TCP Congestion Control with Machine Intelligence

Authors:

Xiaoli MaAuthors Info & Claims

NetAI'18: Proceedings of the 2018 Workshop on Network Meets AI & ML

Pages 60 - 66

https://doi.org/10.1145/3229543.3229550

Published: 07 August 2018 Publication History

Abstract

In a TCP/IP network, a key to ensure efficient and fair sharing of network resources among its users is the TCP congestion control (CC) scheme. Previously, the design of TCP CC schemes is based on hard-wiring of predefined actions to specific feedback signals from the network. However, as networks become more complex and dynamic, it becomes harder to design the optimal feedback-action mapping. Recently, learning-based TCP CC schemes have attracted much attention due to their strong capabilities to learn the actions from interacting with the network. In this paper, we design two learning-based TCP CC schemes for wired networks with under-buffered bottleneck links, a loss predictor (LP) based TCP CC (LP-TCP), and a reinforcement learning (RL) based TCP CC (RL-TCP). We implement both LP-TCP and RL-TCP in NS2. Compared to the existing NewReno and Q-learning based TCP, LP-TCP and RL-TCP both achieve a better tradeoff between throughput and delay, under various simulated network scenarios.

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Cited By

Yi FWan HJamieson KRexford JXie YMichel O(2024)Athena: Seeing and Mitigating Wireless Impact on Video Conferencing and BeyondProceedings of the 23rd ACM Workshop on Hot Topics in Networks10.1145/3696348.3696889(103-110)Online publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1145/3696348.3696889
Zheng CZang MHong XPerreault LBensoussane RVargaftik SBen-Itzhak YZilberman N(2024)Planter: Rapid Prototyping of In-Network Machine Learning InferenceACM SIGCOMM Computer Communication Review10.1145/3687230.368723254:1(2-21)Online publication date: 6-Aug-2024
https://doi.org/10.1145/3687230.3687232
Zhang Hzhuo LLi HZhou AWang CMa HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)AraLive: Automatic Reward Adaption for Learning-based Live Video StreamingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681499(11099-11108)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681499
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Index Terms

Improving TCP Congestion Control with Machine Intelligence
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Reinforcement learning
      2. Supervised learning
    2. Machine learning approaches
      1. Classification and regression trees
2. Networks
  1. Network protocols
    1. Transport protocols

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cover image ACM Conferences

NetAI'18: Proceedings of the 2018 Workshop on Network Meets AI & ML

August 2018

86 pages

ISBN:9781450359115

DOI:10.1145/3229543

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCOMM: ACM Special Interest Group on Data Communication

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Association for Computing Machinery

New York, NY, United States

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Published: 07 August 2018

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SIGCOMM '18

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SIGCOMM

SIGCOMM '18: ACM SIGCOMM 2018 Conference

August 24, 2018

Budapest, Hungary

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Overall Acceptance Rate 13 of 38 submissions, 34%

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Cited By

Yi FWan HJamieson KRexford JXie YMichel O(2024)Athena: Seeing and Mitigating Wireless Impact on Video Conferencing and BeyondProceedings of the 23rd ACM Workshop on Hot Topics in Networks10.1145/3696348.3696889(103-110)Online publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1145/3696348.3696889
Zheng CZang MHong XPerreault LBensoussane RVargaftik SBen-Itzhak YZilberman N(2024)Planter: Rapid Prototyping of In-Network Machine Learning InferenceACM SIGCOMM Computer Communication Review10.1145/3687230.368723254:1(2-21)Online publication date: 6-Aug-2024
https://doi.org/10.1145/3687230.3687232
Zhang Hzhuo LLi HZhou AWang CMa HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)AraLive: Automatic Reward Adaption for Learning-based Live Video StreamingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681499(11099-11108)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681499
Li YChen LSu LZhao KWang JYang YGe N(2024)PEPesc: A TCP Performance Enhancing Proxy for Non-Terrestrial NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2023.326943623:4(3060-3076)Online publication date: Apr-2024
https://doi.org/10.1109/TMC.2023.3269436
Giacomoni LParisis G(2024)Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and ResponsivenessIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621288(1451-1460)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621288
Ppallan JSingh SArunachalam K(2024)A Machine Learning-Based Link Quality Assistance at Transport Layer for High-Frequency NetworksICC 2024 - IEEE International Conference on Communications10.1109/ICC51166.2024.10622673(2743-2748)Online publication date: 9-Jun-2024
https://doi.org/10.1109/ICC51166.2024.10622673
Kamel MAhmed Najm IKhalaf Hamoud A(2024)Congestion Control Prediction Model for 5G Environment Based on Supervised and Unsupervised Machine Learning ApproachIEEE Access10.1109/ACCESS.2024.341686312(91127-91139)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3416863
P. AVimala HJ. S(2024)Comprehensive review on congestion detection, alleviation, and control for IoT networksJournal of Network and Computer Applications10.1016/j.jnca.2023.103749221(103749)Online publication date: Jan-2024
https://doi.org/10.1016/j.jnca.2023.103749
Huang XZhu HYang LYin CLi JLi H(2023)TCP-QNCC: congestion control algorithm based on deep Q-networkFifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)10.1117/12.3009403(58)Online publication date: 16-Oct-2023
https://doi.org/10.1117/12.3009403
Choy SWong B(2023)Evaluating Machine Learning Techniques for Predicting Link Instability in Wireless Networks to Support Live Video Streaming2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)10.1109/WiMob58348.2023.10187772(161-168)Online publication date: 21-Jun-2023
https://doi.org/10.1109/WiMob58348.2023.10187772
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