Congestion Control: A Renaissance with Machine Learning

IEEE Network ◽  
2021 ◽  
pp. 1-8
Author(s):  
Wenting Wei ◽  
Huaxi Gu ◽  
Baochun Li
Keyword(s):  
Machine Learning ◽  
Congestion Control

scholarly journals Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?

2020 ◽  
Vol 10 (18) ◽  
pp. 6164
Author(s):  
Luis Diez ◽  
Alfonso Fernández ◽  
Muhammad Khan ◽  
Yasir Zaki ◽  
Ramón Agüero
Keyword(s):  
Machine Learning ◽  
Congestion Control ◽  
Arrival Time ◽  
Wireless Channel ◽  
Transport Layer ◽  
Transport Protocol ◽  
Length Variation ◽  
State Delay ◽  
Extensive Analysis ◽  
Buffer Length

It is well known that transport protocol performance is severely hindered by wireless channel impairments. We study the applicability of Machine Learning (ML) techniques to predict congestion status of 5G access networks, in particular mmWave links. We use realistic traces, using the 3GPP channel models, without being affected using legacy congestion-control solutions. We start by identifying the metrics that might be exploited from the transport layer to learn the congestion state: delay and inter-arrival time. We formally study their correlation with the perceived congestion, which we ascertain based on buffer length variation. Then, we conduct an extensive analysis of various unsupervised and supervised solutions, which are used as a benchmark. The results yield that unsupervised ML solutions can detect a large percentage of congestion situations and they could thus bring interesting possibilities when designing congestion-control solutions for next-generation transport protocols.

scholarly journals Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 607 ◽  
Author(s):  
Ihab Ahmed Najm ◽  
Alaa Khalaf Hamoud ◽  
Jaime Lloret ◽  
Ignacio Bosch
Keyword(s):  
Machine Learning ◽  
Congestion Control ◽  
Learning Model ◽  
Training Dataset ◽  
Transmission Protocol ◽  
Control Mechanisms ◽  
Next Generation ◽  
Control Approach ◽  
Machine Learning Model ◽  
Prediction Approach

The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.

Machine Learning for Internet Congestion Control: Techniques and Challenges

2019 ◽  
Vol 23 (5) ◽  
pp. 59-64 ◽  
Author(s):  
Lei Zhang ◽  
Yong Cui ◽  
Mowei Wang ◽  
Zhenjie Yang ◽  
Yong Jiang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Congestion Control ◽  
Control Techniques ◽  
Internet Congestion Control ◽  
Internet Congestion

Machine Learning-based Congestion Control Routing Strategy for Healthcare IoT Enabled Wireless Sensor Networks

2021 ◽  
Author(s):  
Kamal Upreti ◽  
Nishant Kumar ◽  
Mohammad Shabbir Alam ◽  
Ankit Verma ◽  
Mauparna Nandan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Congestion Control ◽  
Wireless Sensor ◽  
Routing Strategy
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