Due to the advent of technology, humans now live in the modern age of information and data. In this form of world, different
objects are interlinked to data sources, and every aspect of human’s lives are recorded in a digital form. For example, the present
electronic globe has an abundance of distinct forms of data e.g., health data, social media fata, smartphone data, business data, smart city
data, cybersecurity data and Internet of Things (IoT) data, including Covid-19 data. Data can be unstructured, semi-structured and
structured, and this is increasing on a daily basis. Machine Learning (ML) is significantly employed in different aspects of real-life e.g.,
Congestion Control (CC). This paper provides an evaluation of the aspect ML employed in CC. CC has emerged as a fundamental
viewpoint in communications system infrastructure in the recent years, since network operations, and network capacity have enhanced
at a rapid rate.
Keywords
Congestion Control (CC), Network Congestion (NC), Machine Learning (ML), Transmission Control Protocol (TCP),
Congestion Window (CW).
M. Molinaro and G. Orzes, “From forest to finished products: The contribution of Industry 4.0 technologies to the wood sector,” Comput. Ind., vol. 138, no. 103637, p. 103637, 2022.
B. Xiang, X. Liu, and Y. Chen, “Event-based networked predictive control systems with secure transmission protocol,” Int. J. Control Autom.
Syst., vol. 20, no. 4, pp. 1076–1086, 2022.
L. Khoshnevisan, F. R. Salmasi, and X. Liu, “Integral sliding-mode robust observer-based congestion control for wireless access networks,” J. Contr. Decis., vol. 9, no. 2, pp. 152–164, 2022.
W. Pan, H. Tan, X. Li, and X. Li, “Improved RTT fairness of BBR congestion control algorithm based on adaptive congestion window,”
Electronics (Basel), vol. 10, no. 5, p. 615, 2021.
E. Ancillotti and R. Bruno, “BDP-CoAP: Leveraging bandwidth-delay product for congestion control in CoAP,” in 2019 IEEE 5th World Forum
on Internet of Things (WF-IoT), 2019.
M.-R. Fida, A. F. Ocampo, and A. Elmokashfi, “Measuring and localising congestion in mobile broadband networks,” IEEE trans. netw. serv.
manag., vol. 19, no. 1, pp. 366–380, 2022.
R. Node, “Relay node placement in hierarchical wireless sensor networks,” J. Adv. Comput. Netw., pp. 41–46, 2017.
G. K. Facenda et al., “Adaptive relaying for streaming erasure codes in a three node relay network,” arXiv [cs.IT], 2022.
S. Suma and B. Harsoor, “Detection of malicious activity for mobile nodes to avoid congestion in Wireless Sensor Network,” in 2022 IEEE
Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC), 2022.
P. M. B. Melo, “An enhanced transmission control protocol initial sequence number steganographic method,” J. Adv. Res. Dyn. Control Syst.,
vol. 12, no. 01-Special, pp. 252–259, 2020.
Y. Hinuma, M. Kohyama, and S. Tanaka, “Boundary plane-oriented grain boundary model generation,” Model. Simul. Mat. Sci. Eng., vol. 30,
no. 4, p. 045005, 2022
G. Gaudet, “Forces underlying the evolution of natural resource policies in Quebec,” in Natural Resources in U.S.-Canadian Relations,
Routledge, 2019, pp. 247–265.
“The continuing evolution of immigration and naturalization issues and policies (Asians),” in From All Points, Indiana University Press, 2018,
pp. 124–136.
S. Y.-C. Chen, C.-M. Huang, C.-W. Hsing, H.-S. Goan, and Y.-J. Kao, “Variational quantum reinforcement learning via evolutionary
optimization,” Mach. Learn.: Sci. Technol., vol. 3, no. 1, p. 015025, 2022.
N. M. Šljivić, “Cross-entropy method for estimation of posterior expectation in Bayesian VAR models,” Commun. Stat. Theory Methods, vol.
46, no. 23, pp. 11933–11947, 2017.
Q. Liu, L. Long, Q. Yang, H. Peng, J. Wang, and X. Luo, “LSTM-SNP: A long short-term memory model inspired from spiking neural P
systems,” Knowl. Based Syst., vol. 235, no. 107656, p. 107656, 2022.
X. Long, J. Wang, S. Gong, G. Li, and H. Ju, “Reference evapotranspiration estimation using long short‐term memory network and wavelet‐
coupled long short‐term memory network,” Irrig. Drain., 2022.
Z. Wen, B. Long, K. Lin, and S. Wang, “Equivalent modeling based on long short-term memory neural network for virtual synchronous
generator,” in 2021 33rd Chinese Control and Decision Conference (CCDC), 2021.
K. Fahmi, D. Leith, S. Kucera, and H. Claussen, “Understanding MPTCP in multi-WAN routers: Measurements and system design,” in 2021
IEEE 46th Conference on Local Computer Networks (LCN), 2021.
P. Ladosz et al., “Deep reinforcement learning with modulated Hebbian plus Q-network architecture,” IEEE Trans. Neural Netw. Learn. Syst.,
vol. PP, pp. 1–12, 2021.
B. Zhang, F. Devoti, I. Filippini, and D. De Donno, “Resource allocation in mmWave 5G IAB networks: A reinforcement learning approach
based on column generation,” Comput. netw., vol. 196, no. 108248, p. 108248, 2021.
M. Asghari, J. Bagherzadeh, and S. Yousefi, “Loss estimation and control mechanism in bufferless optical packet-switched networks based on
multilayer perceptron,” Photonic netw. commun., vol. 35, no. 2, pp. 274–286, 2018.
S. Mohapatra, J. An, and R. Gómez-Bombarelli, “Chemistry-informed macromolecule graph representation for similarity computation,
unsupervised and supervised learning,” Mach. Learn.: Sci. Technol., vol. 3, no. 1, p. 015028, 2022.
K. H. Gusti, I. Irhamah, and H. Kuswanto, “Hybrid double seasonal ARIMA and support vector regression in short-term electricity load
forecasting,” Aptech Proceeding Int. Semin. Appl. Technol. Sci. Arts: Dev. Green Agro-Ind. Support Hum. Life Sustain., vol. 0, no. 6, p. 322,
2021.
C. A. Nascimento, A. M. Luiz, P. L. Barros, A. T. P. Neto, and J. J. N. Alves, “A CFD-based empirical model for hazardous area extent prediction
including wind effects,” J. Loss Prev. Process Ind., vol. 71, no. 104497, p. 104497, 2021.
M. Stephan, L. Servadei, J. Arjona-Medina, A. Santra, R. Wille, and G. Fischer, “Scene-adaptive radar tracking with deep reinforcement
learning,” Machine Learning with Applications, vol. 8, no. 100284, p. 100284, 2022.
Acknowledgements
Authors thank Reviewers for taking the time and effort necessary to review the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Madeleine Wang Yue Dong
Madeleine Wang Yue Dong
School of Design, University of Washington, Seattle, WA.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Madeleine Wang Yue Dong and Yannis Yortsos, “Application of Machine Learning Technologies for Transport layer Congestion Control", vol.2, no.2, pp. 066-076, April 2022. doi: 10.53759/181X/JCNS202202010.
